CN108550007B - Goods space optimization method and system for automatic stereoscopic warehouse of pharmaceutical enterprise - Google Patents

Goods space optimization method and system for automatic stereoscopic warehouse of pharmaceutical enterprise Download PDF

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CN108550007B
CN108550007B CN201810299910.2A CN201810299910A CN108550007B CN 108550007 B CN108550007 B CN 108550007B CN 201810299910 A CN201810299910 A CN 201810299910A CN 108550007 B CN108550007 B CN 108550007B
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贺建军
曾琦
胡恩泽
曹星宇
刘新
阳春华
桂卫华
王宏伟
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Abstract

The invention discloses a goods space optimizing method and a goods space optimizing system for an automatic stereoscopic warehouse of a pharmaceutical enterprise, the method comprises the steps of establishing a goods space optimizing target of the automatic stereoscopic warehouse, calculating the frequency of entering and leaving the warehouse of medicines according to historical order data of the medicines in the automatic stereoscopic warehouse, obtaining a correlation factor between each type of medicines according to the correlation degree between each type of medicines, establishing a stacker movement mathematical model, establishing a multi-target goods space optimizing mathematical model according to the stacker movement mathematical model, the frequency of entering and leaving the warehouse of the medicines and the correlation factor between each type of medicines, solving the multi-target goods space optimizing mathematical model to obtain a goods space optimizing result, solving the problems that the prior method only considers the goods turnover rate and the shelf stability, cannot well describe the actual problem and causes the optimizing result to be unsatisfactory, and the goods optimizing result obtained based on the multi-target goods space optimizing model is more ideal, the goods are distributed more reasonably, the warehousing operation efficiency is greatly improved, and the warehousing operation cost is reduced.

Description

Goods space optimization method and system for automatic stereoscopic warehouse of pharmaceutical enterprise
Technical Field
The invention mainly relates to the technical field of logistics storage, in particular to a goods space optimization method and a goods space optimization system for an automatic stereoscopic warehouse of a pharmaceutical enterprise.
Background
With the increasing supply chain overall level and the increasing industry standard (GMP/GSP) in the pharmaceutical industry, pharmaceutical logistics is becoming a key area for logistics automation. The automatic stereoscopic warehouse is used as an important component of modern logistics, adopts mechanical operation and informatization scheduling, has the advantages of saving labor force, improving the warehousing management level, reducing logistics cost and the like, is an advanced warehousing mode widely accepted by society, and is widely applied to enterprise warehousing. And the automatic stereoscopic warehouse of the pharmaceutical enterprise is connected with the production workshop through an aerial conveying line, so that the whole process from raw materials to finished products of logistics in the enterprise is automated, and the caching requirement of goods before packaging and unloading to loading is met. Effectively managing and controlling warehousing costs is one of the most effective means for enterprises to obtain profits.
With the application of electronic tag auxiliary systems and radio communication transmission technologies, the picking speed and efficiency are continuously improved, and most of the warehousing cost is more consumed in moving the goods in the warehouse. The moving time of the goods in the warehouse becomes one of the key factors for improving the operation efficiency of the warehouse. The reasonable layout of the goods can effectively reduce the carrying distance of the transporting equipment such as the stacker and the like, and reduce the loss of the goods in the storage process and the carrying process. However, as goods continuously enter and exit the warehouse, vacancy occurs in some goods positions; due to the occurrence of seasonal diseases such as influenza or sudden infectious diseases, the frequency of goods entering and leaving the warehouse is changed; due to the fact that some goods are newly added or some goods exit the market, the storage positions of the warehouse are crowded or vacant, and the goods are not placed in the storage positions. The goods location optimization is to dynamically reconfigure the goods locations of the goods in the warehouse based on the variation factors so as to ensure that the goods location layout is in a reasonable state. Therefore, the goods location optimization is carried out on the warehouse regularly, and the method has important significance for improving the warehousing operation efficiency and reducing the warehousing operation cost.
The key point of the goods space optimization lies in establishing an optimization model which accords with the characteristics of the automatic stereoscopic warehouse of the pharmaceutical enterprise, and the core of the optimization model lies in reasonably establishing a mathematical model of a plurality of optimization targets and designing a corresponding optimization algorithm according to the mathematical model. The establishment of the mathematical model of a plurality of optimization targets depends on the analysis of the drug specificity of pharmaceutical enterprises, the characteristics of the automatic stereoscopic warehouse and the deep analysis of goods related data, particularly the analysis and utilization of historical data. The unit weight of the medicines of the pharmaceutical enterprises is light, and the weight difference among the medicines does not need to be considered. In the goods yard optimization process, the operation time of the stacker is a main factor influencing the operation efficiency. Moreover, along with the change of seasons, the demand varieties, the demand quantity and the demand frequency of goods in the warehouse of the pharmaceutical enterprise are greatly changed at different periods. Therefore, by combining the particularity of medicines of pharmaceutical enterprises and considering the change of the running speed of the stacker in the goods space optimization process, reasonable analysis and utilization of historical data are the crucial prerequisites for designing an accurate control algorithm and realizing the goods space optimization of the automatic stereoscopic warehouse. In a general goods location optimization process, a goods location optimization mathematical model is mostly established according to the turnover rate of goods and the stability of a goods shelf, and then an optimization algorithm is utilized to carry out optimization solution. However, the particularity of medicines of pharmaceutical enterprises is not considered in the goods space optimization process, the change of the operating speed of the stacker in the goods space optimization process is not considered, and the association degree between the medicines is not considered. Therefore, for the goods space optimization method, the particularity of the optimization object is not fully considered, and the historical data of the automatic stereoscopic warehouse is not fully utilized, so that the optimization effect is not ideal, and the operation efficiency of warehousing and the operation cost of warehousing are influenced.
Disclosure of Invention
The goods location optimization method and the goods location optimization system for the automatic stereoscopic warehouse of the pharmaceutical enterprise solve the problems that the actual problems cannot be well described and the optimization results are not ideal due to the fact that the existing method only considers the goods turnover rate and the shelf stability.
In order to solve the technical problem, the goods space optimization method of the automatic stereoscopic warehouse of the pharmaceutical enterprise provided by the invention comprises the following steps:
the goods space optimization system of the automatic stereoscopic warehouse of the pharmaceutical enterprise provided by the invention comprises:
establishing a goods space optimization target of the automatic stereoscopic warehouse;
calculating the frequency of the drugs entering and leaving the warehouse according to the historical order data of the drugs in the automatic three-dimensional warehouse;
classifying the medicines in the automatic three-dimensional warehouse, and obtaining a correlation factor between each type of medicines according to the correlation degree between each type of medicines;
establishing a stacker movement mathematical model according to the change of the movement speed of the stacker in the cargo space optimization process;
establishing a multi-target cargo space optimization mathematical model according to the piler movement mathematical model, the drug warehouse-in and warehouse-out frequency and the correlation factor among each type of drugs;
and solving the multi-target cargo space optimization mathematical model to obtain a cargo space optimization result.
Further, the objective of establishing the goods space optimization of the automated stereoscopic warehouse is specifically as follows:
and establishing the frequency of the drugs entering and leaving the automated stereoscopic warehouse and the relevance of the drugs as the goods space optimization target of the automated stereoscopic warehouse.
Further, a calculation formula for calculating the warehousing-in and warehousing-out frequency of the medicines according to the historical order data of the medicines in the automatic stereoscopic warehouse is as follows:
Figure GDA0003102090440000021
wherein p isijFrequency of warehousing for ith class jth drug, MijThe total number of the in-out warehouse of the ith type jth medicine in the corresponding production period is S, and the total number of the in-out warehouse of all the medicines in the corresponding production period is S.
Further, classifying the drugs in the automated stereoscopic warehouse, and obtaining a correlation factor between each class of drugs according to the degree of correlation between each class of drugs includes:
dividing all the medicines in the automatic three-dimensional warehouse into n types, wherein the ith type has ki medicines;
calculating the correlation factor between the medicines in each class as
Figure GDA0003102090440000031
Wherein r isifgIs the correlation factor, f, g (f, g e 1, 2.., j.. k.) of two drugs f, g in class ii) Q is the total number of orders in the corresponding production cycle, QifgThe number of orders containing the medicines f and g at the same time;
obtaining the correlation matrix of the correlation factors of the medicines of the same type according to the correlation factors among the medicines of each type as follows:
Figure GDA0003102090440000032
acquiring a medicine e with the strongest degree of association with other medicines in the ith class according to the association matrix;
calculating the correlation factor of other medicines in the ith class relative to medicine e and recording the correlation factor as
Figure GDA0003102090440000033
Further, according to the change of the movement speed of the stacker in the cargo space optimization process, the building of the stacker movement mathematical model comprises the following steps:
when the stacker picks the ith medicine and the jth medicine, the time spent in the horizontal direction is as follows:
Figure GDA0003102090440000034
Figure GDA0003102090440000035
sxij＝xijl
wherein, txijTime spent in horizontal direction, s, for picking class i jth drugs for stackerxijThe moving distance in the horizontal direction, a, when picking the ith and jth medicines for the stackerxAcceleration of the stacker in the horizontal direction, vxmaxThe maximum running speed of the stacker in the horizontal direction, SxmaxFor uniform acceleration of stacker to vxmaxMaximum horizontal travel distance of time, xijIs the coordinate value of the ith class jth medicine in the horizontal direction, and l is the length of the goods grid;
when the stacker picks the ith medicine and the jth medicine, the time spent in the vertical direction is as follows:
Figure GDA0003102090440000041
Figure GDA0003102090440000042
szij＝zijh
wherein, tzijTime spent in vertical direction, s, for picking class i jth drugs for stackerzijThe moving distance in the vertical direction, a, when picking the ith and jth medicines for the stackerzAcceleration of the stacker in the vertical direction, vzmaxThe maximum running speed of the stacker in the vertical direction, SzmaxFor uniform acceleration of stacker to vzmaxMaximum vertical travel distance of time, zijIs the vertical direction of the ith medicineH is the height of the cargo grid;
the time taken for the stacker to pick the ith class jth drug is:
tij＝max(txij,tzij)。
further, according to a movement mathematical model of the stacker, the warehousing and ex-warehousing frequency of the medicines and the correlation factor among each type of medicines, a multi-target cargo space optimization mathematical model is established as follows:
Figure GDA0003102090440000043
at the same time, the user can select the desired position,
Figure GDA0003102090440000051
and i, j, xij,yij,zijAre all integers, and are not limited to the whole number,
wherein n is the category number of the medicines in the automatic three-dimensional warehouse, kiIs the number of i-th class of drugs, pijFrequency of warehousing for ith class jth drug, tijThe time it takes for the stacker to pick the ith class jth drug, (x)ij，yij，zij) For the optimized location, v, of the ith class of jth drugyIn order to realize the uniform transportation speed of the conveyor belt,
Figure GDA0003102090440000052
the optimized positions of the medicines with the strongest degree of association with other medicines in the class i are r and w are the distance between the roadways and the width of the cargo space,
Figure GDA0003102090440000053
a, B, C is the maximum number of columns, rows and layers of the warehouse, respectively, for the average location of all the drugs.
Further, solving the multi-target cargo space optimization mathematical model specifically comprises: and solving the multi-target cargo space optimization mathematical model by adopting an NSGA-II algorithm.
Further, solving the multi-target cargo space optimization mathematical model by adopting an NSGA-II algorithm comprises the following steps:
adopting integer coding to code the position of the goods space where the medicine is located in the automatic three-dimensional warehouse, wherein the population is a matrix, each action is a chromosome and corresponds to a feasible solution, and the population number, the maximum evolution algebra, the cross probability and the variation probability are set;
randomly generating an initial population by adopting a Mersene rotation algorithm (Mersene Twister), taking the reciprocal of an objective function in a multi-objective cargo space optimization mathematical model as a fitness function, calculating an individual fitness value, performing fast Non-Dominated Sort (Non-Dominated Sort), and calculating the Crowding (crowning Distance);
forming a new population by adopting a binary Tournament Selection strategy (Tournament Selection);
respectively carrying out crossing and mutation operations by adopting a simulated binary crossing operator (SBX) and a polynomial mutation operator (polymonomial mutation) to generate a progeny population;
combining the parent population and the offspring population into a temporary population, then carrying out non-dominated sorting, calculating the crowding degree distance, and selecting a new parent population by adopting a crowding degree comparison operator;
on the basis, selection, crossing and mutation operations are carried out to form a new filial generation population, and if the current evolution generation is greater than the maximum evolution generation, the evolution is stopped to obtain a group of Pareto optimal solution sets.
The invention provides a goods space optimization system of an automatic stereoscopic warehouse of a pharmaceutical enterprise, which comprises:
the storage, the processor and the computer program stored on the storage and capable of running on the processor, when the processor executes the computer program, the steps of the cargo space optimization method for the pharmaceutical enterprise automatic stereoscopic warehouse are realized.
Compared with the prior art, the invention has the advantages that:
the invention provides a goods space optimization method and a goods space optimization system for an automatic stereoscopic warehouse of a pharmaceutical enterprise, which are characterized in that a goods space optimization target of the automatic stereoscopic warehouse is established, the frequency of putting and taking medicines out of the warehouse is calculated according to historical order data of the medicines in the automatic stereoscopic warehouse, the medicines in the automatic stereoscopic warehouse are classified, a correlation factor between each kind of medicines is obtained according to the correlation degree between each kind of medicines, a stacker movement mathematical model is established according to the change of the movement speed of a stacker in the goods space optimization process, a multi-target goods space optimization mathematical model is established according to the stacker movement mathematical model, the frequency of putting medicines in and out of the warehouse and the correlation factor between each kind of medicines, and the multi-target goods space optimization mathematical model is solved to obtain a goods space optimization result, so that the problem that the actual problem cannot be well described due to the consideration of the goods turnover rate and the shelf stability of the existing method is solved, the problem that the optimization result is not ideal is caused, and the established multi-target goods location optimization mathematical model fully considers the particularity of medicines of pharmaceutical enterprises, the characteristics of an automatic stereoscopic warehouse and the actual working condition, so that the goods optimization result obtained by solving based on the multi-target goods location optimization mathematical model is more ideal, the goods distribution is more reasonable, the warehousing operation efficiency is greatly improved, and the warehousing operation cost is reduced.
Drawings
Fig. 1 is a flowchart of a method for optimizing a cargo space of an automated stereoscopic warehouse of a pharmaceutical enterprise according to a first embodiment of the present invention;
fig. 2 is a flowchart of a cargo space optimization method for an automated stereoscopic warehouse of a pharmaceutical enterprise according to a second embodiment of the present invention;
FIG. 3 is a flowchart of an algorithm for solving a multi-objective cargo space optimization mathematical model by using an NSGA-II algorithm according to a second embodiment of the present invention;
fig. 4 is a distribution diagram of the cargo space before medicine optimization in the cargo space optimization method for the automated stereoscopic warehouse of the pharmaceutical enterprise according to the second embodiment of the present invention;
fig. 5 is a distribution diagram of the cargo space after drug optimization in the cargo space optimization method for the automated stereoscopic warehouse of the pharmaceutical enterprise according to the second embodiment of the present invention;
fig. 6 is a block diagram illustrating a cargo space optimization system of an automated stereoscopic warehouse of a pharmaceutical enterprise according to an embodiment of the present invention.
Reference numerals:
10. a memory; 20. a processor.
Detailed Description
In order to facilitate an understanding of the invention, the invention will be described more fully and in detail below with reference to the accompanying drawings and preferred embodiments, but the scope of the invention is not limited to the specific embodiments below.
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Example one
Referring to fig. 1, a method for optimizing a cargo space of an automated stereoscopic warehouse of a pharmaceutical enterprise according to an embodiment of the present invention includes:
s101, determining a goods space optimization target of an automatic stereoscopic warehouse;
step S102, calculating the frequency of entering and leaving the drug according to the historical order data of the drug in the automatic three-dimensional warehouse;
step S103, classifying the medicines in the automatic three-dimensional warehouse, and obtaining a correlation factor between each type of medicines according to the correlation degree between each type of medicines;
step S104, establishing a stacker movement mathematical model according to the change of the movement speed of the stacker in the cargo space optimization process;
s105, establishing a multi-target cargo space optimization mathematical model according to the piler movement mathematical model, the drug warehouse-in and warehouse-out frequency and the correlation factor among each type of drugs;
and S106, solving the multi-target cargo space optimization mathematical model to obtain a cargo space optimization result.
The goods space optimization method of the automatic stereoscopic warehouse of the pharmaceutical enterprise provided by the embodiment of the invention comprises the steps of establishing a goods space optimization target of the automatic stereoscopic warehouse, calculating the frequency of putting and taking medicines out of the warehouse according to historical order data of the medicines in the automatic stereoscopic warehouse, classifying the medicines in the automatic stereoscopic warehouse, obtaining a correlation factor between each type of medicines according to the correlation degree between each type of medicines, establishing a stacker movement mathematical model according to the change of the movement speed of a stacker in the goods space optimization process, establishing a multi-target goods space optimization mathematical model according to the stacker movement mathematical model, the frequency of putting medicines in and out of the warehouse and the correlation factor between each type of medicines, solving the multi-target goods space optimization mathematical model to obtain a goods space optimization result, solving the problem that the actual problem cannot be well described by only considering the goods turnover rate and shelf stability in the existing method, the problem that the optimization result is not ideal is caused, and the established multi-target goods location optimization mathematical model fully considers the particularity of medicines of pharmaceutical enterprises, the characteristics of an automatic stereoscopic warehouse and the actual working condition, so that the goods optimization result obtained by solving based on the multi-target goods location optimization mathematical model is more ideal, the goods distribution is more reasonable, the warehousing operation efficiency is greatly improved, and the warehousing operation cost is reduced.
Specifically, according to the requirements of the pharmaceutical enterprises, the embodiment of the invention combines the specificity of the medicines of the pharmaceutical enterprises to establish the goods space optimization target of the automatic stereoscopic warehouse. The specificity of the medicines of pharmaceutical enterprises is represented by the following aspects:
according to the requirements of GMP/GSP standards, medicines need to be classified and stored according to different natural attributes.
Secondly, the unit weight of the medicine is mostly smaller, and the weight of the medicine can not be considered when the goods space is optimized.
And thirdly, the relevance among the medicines is strong, and the medicines with strong relevance are placed together in order to ensure the access efficiency.
Therefore, when establishing the optimization goal, first, the medicine with a high frequency of entering and exiting the warehouse should be placed closer to the entrance, so as to shorten the time taken for the stacker to pick the goods and improve the working efficiency of warehousing. At the same time, the weight difference between the drugs, i.e. the shelf stability, is no longer taken into account. Then, on the basis of medicine classified storage, the association degree of all medicines in each class is analyzed, so that the medicines with strong intra-class association degree are put together, the time spent on picking the same batch of goods in and out of the warehouse is shortened, the operation efficiency of warehousing is further improved, and the operation cost of warehousing is reduced. Thus, the frequency of drug storage and retrieval and the correlation between drugs are established as optimization targets.
Example two
Referring to fig. 2, a second embodiment of the present invention provides a method for optimizing a cargo space of an automated stereoscopic warehouse of a pharmaceutical enterprise, including:
step S201, determining the frequency of the drugs entering and leaving the automated stereoscopic warehouse and the relevance of the drugs as the goods space optimization target of the automated stereoscopic warehouse.
And step S202, calculating the frequency of the drugs entering and exiting the warehouse according to the historical order data of the drugs in the automatic three-dimensional warehouse.
Specifically, in a production cycle, the frequency of warehousing and warehousing of the ith and jth medicines is as follows:
Figure GDA0003102090440000081
wherein p isijFrequency of warehousing for ith class jth drug, MijThe total number of the in-out warehouse of the ith type jth medicine in the corresponding production period is S, and the total number of the in-out warehouse of all the medicines in the corresponding production period is S.
And step S203, classifying the medicines in the automatic three-dimensional warehouse, and obtaining a correlation factor between each type of medicines according to the correlation degree between each type of medicines.
Specifically, the medicines in the automatic three-dimensional warehouse are classified according to the medicine classification storage principle. Dividing all drugs into n classes, i class has kiAnd (4) each medicine. For the ith class and jth drug, the optimized position is (x)ij，yij，zij). In the same production cycle, two drugs f, g (f, g e 1, 2.. j.. k) in the ith classi) The ratio of the number of orders appearing in the same order to the total number of orders is the support of the association rule. For convenience of calculation and expression, two drugs f, g with the support degree of the ith class are defined as:
Figure GDA0003102090440000082
wherein, at the same time, the total amount of orders in the corresponding production period is Q, and also includesThe order number of the medicines f and g is qifg。
Therefore, the correlation factors among all the medicines in the ith class in the time period obtain the correlation matrix of the correlation factors of the medicines of the same class:
Figure GDA0003102090440000083
selecting the medicine e with the strongest degree of association with other medicines in the ith class according to the association matrix, and recording the optimized position as the position
Figure GDA0003102090440000084
From this, the correlation factor of the i-th class of other drugs with drug e is obtained and recorded as
Figure GDA0003102090440000085
And step S204, establishing a stacker movement mathematical model according to the change of the movement speed of the stacker in the cargo space optimization process.
Specifically, in this embodiment, according to an actual working condition, a change of a movement speed of the stacker in the cargo space optimization process is analyzed, a movement process of picking a cargo by the stacker is abstracted into an even acceleration and deceleration process, and a movement mathematical model of the stacker is established as follows:
when the stacker picks the ith class jth medicine, the time spent in the horizontal direction is as follows:
Figure GDA0003102090440000091
Figure GDA0003102090440000092
sxij＝xij l (5)
wherein, txijWhen sorting ith class and jth medicine for stacker, the medicine is sorted horizontallyTime taken, sxijThe moving distance in the horizontal direction, a, when picking the ith and jth medicines for the stackerxAcceleration of the stacker in the horizontal direction, vxmaxThe maximum running speed of the stacker in the horizontal direction, SxmaxFor uniform acceleration of stacker to vxmaxMaximum horizontal travel distance of time, xijIs the coordinate value of the ith class jth medicine in the horizontal direction, and l is the length of the goods grid;
similarly, when the stacker picks the ith class and jth medicine, the time spent in the vertical direction is as follows:
Figure GDA0003102090440000093
Figure GDA0003102090440000094
szij＝zij h (8)
wherein, tzijTime spent in vertical direction, s, for picking class i jth drugs for stackerzijThe moving distance in the vertical direction, a, when picking the ith and jth medicines for the stackerzAcceleration of the stacker in the vertical direction, vzmaxThe maximum running speed of the stacker in the vertical direction, SzmaxFor uniform acceleration of stacker to vzmaxMaximum vertical travel distance of time, zijIs a coordinate value of the ith class and the jth medicine in the vertical direction, and h is the height of the cargo grid;
then, the time it takes for the stacker to pick the ith class jth drug is:
tij＝max(txij,tzij) (9)
and S205, establishing a multi-target cargo space optimization mathematical model according to the piler movement mathematical model, the drug warehouse-in and warehouse-out frequency and the correlation factor among each type of drugs.
In order to realize the near delivery and storage of the medicines, the medicines with high delivery and storage frequency are stored at the position near the delivery and storage table, so that the sum of the delivery and storage frequency of the medicines and the time spent for sorting the medicines is minimum, and each cargo compartment is supposed to be only stored with one medicine, the time from a stacker to a conveyor belt is ignored, namely the time from the stacker to the conveyor belt is
Figure GDA0003102090440000101
Wherein n is the number of classes of the drugs in the automatic three-dimensional warehouse, kiIs the number of i-th class of drugs, pijFrequency of warehousing for ith class jth drug, tijThe time it takes for the stacker to pick the ith class jth drug, (x)ij，yij，zij) For the optimized location, v, of the ith class of jth drugyThe uniform transport speed of the conveyor belt is r and w are the distance between the roadways and the width of the goods grids.
According to the strong relevance among the medicines, on the basis of medicine classification storage, the relevance degree among all the medicines in each class is analyzed by adopting a relevance analysis method according to historical data, so that the medicines with strong relevance degree in the class are put together, and the time spent on picking the same batch of goods in and out of the warehouse is shortened.
For all the medicines of the ith class, with the medicine e as a center position, calculating Euclidean distances between other medicines of the class and the medicine e, and taking the obtained association factor as an association weight, so that the sum of products of the Euclidean distances between other medicines of the class and the medicine e and the corresponding association weight is minimum, namely:
Figure GDA0003102090440000102
in order to make all the medicines in the warehouse nearest to the warehouse inlet and outlet as much as possible, the Euclidean distance between the average position of all the medicines and the warehouse inlet and outlet is minimized, namely:
Figure GDA0003102090440000103
further, it is possible to prevent the occurrence of,
Figure GDA0003102090440000104
wherein the content of the first and second substances,
Figure GDA0003102090440000111
the optimized position of the drug with the strongest degree of association with other drugs in the class i,
Figure GDA0003102090440000112
represents the average position of all the medicines, and d represents the Euclidean distance from the exit/entrance of the average position of all the medicines.
Meanwhile, in order to store each type of medicine in a dispersed manner as much as possible, the sum of the euclidean distances between the center position of each type of medicine and the average position of all medicines is maximized, that is:
Figure GDA0003102090440000113
thereby, the following were obtained:
Figure GDA0003102090440000114
in conclusion, a cargo space optimization mathematical model with multiple optimization targets is obtained:
Figure GDA0003102090440000115
at the same time, the user can select the desired position,
Figure GDA0003102090440000116
and i, j, xij,yij,zijAre all integers, and are not limited to the whole number,
a, B, C represents the maximum number of columns, the maximum number of rows, and the maximum number of layers of the warehouse, respectively.
And S206, solving the multi-target cargo space optimization mathematical model by adopting an NSGA-II algorithm to obtain a cargo space optimization result.
Referring to fig. 3, fig. 3 is an algorithm flowchart for solving the multi-objective cargo space optimization mathematical model by using the NSGA-ii algorithm according to the embodiment of the present invention, and the specific steps are as follows:
coding the position of the goods space where the medicine is located by adopting integer coding, wherein the population is a matrix, and each action corresponds to a chromosome and a feasible solution. And setting the population number, the maximum evolution algebra, the cross probability and the variation probability.
The information of each medicine comprises the code of the medicine, the storage position code and the goods position number of the medicine. Because the mapping relation exists between the medicines and the goods positions where the medicines are located, the goods code of each medicine is fixed and unchanged, and the storage position code and the goods position number where the medicines are located are dynamically changed along with the movement of the medicines. Therefore, the invention adopts integer coding, and selects the goods space of the position of the medicine as the gene on the chromosome:
1) the population is a matrix, each action is a chromosome and corresponds to a feasible solution, namely corresponds to a goods space optimization scheme;
2) the number of genes contained in each chromosome represents the number of the goods to be optimized;
3) each gene on each chromosome represents the goods position information of the position of a medicine, and respectively represents the column, row and row of the position of the medicine. That is, each gene is represented by 3 integers.
As shown in table 1:
TABLE 1 chromosomal coding
Figure GDA0003102090440000121
As shown in table 1, 153 denotes (1, 5, 3), and the medicine with the order number of 1 is stored in the 1 st column, 5 th row, and 3 rd row.
② adopting a Merson rotation algorithm (Mersenne Twister) to randomly generate an initial population. And calculating the individual fitness value by taking the reciprocal of the objective function as a fitness function, performing Non-dominant Sort (Non-dominant Sort) and calculating the Crowding Distance (crown Distance).
In genetic algorithms, Fitness (Fitness) is used to measure how well individuals in a population achieve or approach an optimal solution in an optimization calculation. The probability that the individual with higher fitness is inherited to the next generation is higher; while the probability that a lower individual will be inherited to the next generation is relatively small. The main basis for genetic algorithms to guide searches is the fitness value of an individual. That is, genetic algorithms rely on selection operations to guide the search direction of the algorithm. And in the selection operation, the fitness value of an individual is used as a certainty index, and the individual with high fitness value is selected from the current population for crossing and mutation to find the optimal solution. In this embodiment, all three objective functions are the minimum value of the global system, so that the reciprocal of the objective function is taken as the fitness function, and the corresponding fitness function after the objective function is transformed is:
Figure GDA0003102090440000122
wherein, F1、F2、F3Each corresponding to each objective function in equation (16).
And forming a new population by adopting a binary Tournament Selection strategy (tour Selection).
And fourthly, respectively carrying out crossing and mutation operations by adopting a simulated binary crossing operator (SBX) and a polynomial mutation operator (polynominal mutation) to generate a progeny population.
Combining the parent population and the child population into a temporary population, then carrying out non-dominated sorting, and calculating the crowding distance. And selecting a new parent population by adopting a congestion degree comparison operator.
And sixthly, performing selection, crossing and variation operations on the basis to form a new offspring population. And if the current evolution algebra is larger than the maximum evolution algebra, stopping evolution to obtain a group of Pareto optimal solution sets.
The cargo space optimization method of the present invention is described in detail below with reference to an example:
the working condition parameters of the automatic stereoscopic warehouse finished product warehouse of the pharmaceutical enterprise are shown in table 2.
Table 2 example simulation parameter information table
Simulation parameters Value taking Simulation parameters Value taking
Maximum running speed m/s of stacker in horizontal direction 2.0 Maximum column number of three-dimensional goods shelf 30
Maximum running speed m/s of stacker in vertical direction 0.6 Maximum number of rows of three-dimensional goods shelves 20
Horizontal direction acceleration m/S of stacker2 0.3 Maximum number of layers of three-dimensional goods shelf 10
Acceleration m/S of stacker in vertical direction2 0.5 Position coordinates of warehouse-in/out table (0，0，0)
Running speed m/s of conveyor belt 0.5 Number of groups 100
Length m of each cargo space 1.0 Maximum evolution algebra 500
Width m of each cargo space 1.2 Probability of crossing 0.8
Height m of each cargo space 1.5 Probability of variation 0.1
The known initial data of the drug to be optimized are shown in table 3.
TABLE 3 known initial data of the drug to be optimized
Figure GDA0003102090440000131
Figure GDA0003102090440000141
And (3) optimizing the cargo space of the cargo in the warehouse by using the cargo space optimizing models provided by the formulas (1) to (16), and simulating by adopting MATLAB software.
The method of the second embodiment of the invention is adopted to carry out the goods space optimization of the automatic stereoscopic warehouse of the pharmaceutical enterprise, and the goods space positions before and after the drug optimization are shown in figures 4 and 5, wherein
Figure GDA0003102090440000142
Represents a class 1 drug substance which is,
Figure GDA0003102090440000143
represents a class 2 drug substance to be administered,
Figure GDA0003102090440000144
represents class 3 drugs. It is obvious from fig. 4 and 5 that the optimized medicine is more reasonable in goods location, the same kind of medicines are stored together, the different kinds of medicines are stored in a dispersed manner, and the optimized medicine goods location is closer to the warehouse-in/out table as a whole.
Therefore, the goods location optimization method for the automatic stereoscopic warehouse of the pharmaceutical enterprise, provided by the embodiment of the invention, has the advantages that the goods distribution is more reasonable, the problems that the actual problem cannot be well described and the optimization result is not ideal due to the fact that the goods turnover rate and the shelf stability are only considered in the existing method are solved, and the established multi-target goods location optimization mathematical model fully considers the particularity of medicines of the pharmaceutical enterprise, the characteristics of the automatic stereoscopic warehouse and the actual working condition, so that the goods optimization result obtained by solving based on the multi-target goods location optimization mathematical model is more ideal, the goods distribution is more reasonable, the warehousing operation efficiency is greatly improved, and the warehousing operation cost is reduced.
Referring to fig. 6, the cargo space optimization system of the automated stereoscopic warehouse of the pharmaceutical enterprise according to the embodiment of the present invention includes:
the storage 10, the processor 20, and a computer program stored on the storage 10 and operable on the processor 20, wherein the processor 20 when executing the computer program implements the steps of the method for optimizing the cargo space of the automated stereoscopic warehouse of the pharmaceutical enterprise according to the embodiment of the present invention.
The specific working process and working principle of the cargo space optimization system of the automated stereoscopic warehouse of the pharmaceutical enterprise in this embodiment may refer to the working process and working principle of the cargo space optimization method of the automated stereoscopic warehouse of the pharmaceutical enterprise in this embodiment.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to 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.

Claims (5)

1. A goods space optimization method of an automatic stereoscopic warehouse of a pharmaceutical enterprise is characterized by comprising the following steps:
establishing a goods space optimization target of the automatic stereoscopic warehouse, wherein the goods space optimization target of the automatic stereoscopic warehouse is specifically as follows: establishing the frequency of the drugs entering and leaving the automated stereoscopic warehouse and the relevance of the drugs as the goods space optimization target of the automated stereoscopic warehouse;
calculating the frequency of entering and exiting the drug according to the historical order data of the drug in the automatic stereoscopic warehouse, wherein the calculation formula for calculating the frequency of entering and exiting the drug according to the historical order data of the drug in the automatic stereoscopic warehouse is as follows:
Figure FDA0003102090430000011
wherein p isijFrequency of warehousing for ith class jth drug, MijThe total number of the in-out warehouse of the ith type jth medicine in the corresponding production period is S, and the total number of the in-out warehouse of all the medicines in the corresponding production period is S;
classifying the medicines in the automatic stereoscopic warehouse, and obtaining a correlation factor between each type of medicines according to the correlation degree between each type of medicines, wherein the classifying the medicines in the automatic stereoscopic warehouse, and obtaining the correlation factor between each type of medicines according to the correlation degree between each type of medicines comprises: dividing all drugs in the automated stereoscopic warehouse into n classes, wherein the ith class has kiEach medicine; meterCalculating the correlation factor between the medicines in each class as
Figure FDA0003102090430000012
Wherein r isifgIs a correlation factor of two drugs f, g in class i, where f, g ∈ 1,2iQ is the total number of orders in the corresponding production cycle, QifgThe number of orders containing the medicines f and g at the same time; obtaining the correlation matrix of the correlation factors of the medicines of the same type according to the correlation factors among the medicines of each type as follows:
Figure FDA0003102090430000013
obtaining the medicine e with the strongest degree of association with other medicines in the ith class according to the incidence matrix, calculating the association factor of other medicines in the ith class relative to the medicine e, and recording the association factor as the medicine e
Figure FDA0003102090430000014
Establishing a stacker movement mathematical model according to the change of the movement speed of the stacker in the cargo space optimization process;
establishing a multi-target cargo space optimization mathematical model according to the piler movement mathematical model, the medicine warehouse-in and warehouse-out frequency and the correlation factor among each type of medicines;
and solving the multi-target cargo space optimization mathematical model to obtain a cargo space optimization result.
2. The method for optimizing the cargo space of the automated stereoscopic warehouse of the pharmaceutical enterprise according to claim 1, wherein the step of establishing a mathematical model of the movement of the stacker according to the change of the movement speed of the stacker in the cargo space optimization process comprises the steps of:
when the stacker picks the ith medicine and the jth medicine, the time spent in the horizontal direction is as follows:
Figure FDA0003102090430000021
Figure FDA0003102090430000022
sxij＝xijl
wherein, txijTime spent in horizontal direction, s, for picking class i jth drugs for stackerxijThe moving distance in the horizontal direction, a, when picking the ith and jth medicines for the stackerxAcceleration of the stacker in the horizontal direction, vxmaxThe maximum running speed of the stacker in the horizontal direction, SxmaxFor uniform acceleration of stacker to vxmaxMaximum horizontal travel distance of time, xijIs the coordinate value of the ith class jth medicine in the horizontal direction, and l is the length of the goods grid;
when the stacker picks the ith medicine and the jth medicine, the time spent in the vertical direction is as follows:
Figure FDA0003102090430000023
Figure FDA0003102090430000024
szij＝zijh
wherein, tzijTime spent in vertical direction, s, for picking class i jth drugs for stackerzijThe moving distance in the vertical direction, a, when picking the ith and jth medicines for the stackerzAcceleration of the stacker in the vertical direction, vzmaxThe maximum running speed of the stacker in the vertical direction, SzmaxFor uniform acceleration of stacker to vzmaxMaximum vertical travel distance of time, zijIs a coordinate value of the ith class and the jth medicine in the vertical direction, and h is the height of the cargo grid;
the time taken for the stacker to pick the ith class jth drug is:
tij＝max(txij,tzij)。
3. the method for optimizing the cargo space of the automated stereoscopic warehouse of the pharmaceutical enterprise according to claim 2, wherein the multi-objective cargo space optimization mathematical model is established according to a movement mathematical model of the stacker, the warehousing and ex-warehousing frequency of the medicines and the correlation factor between each type of medicines, and comprises the following steps:
Figure FDA0003102090430000031
at the same time, the user can select the desired position,
Figure FDA0003102090430000032
and i, j, xij,yij,zijAre all integers, and are not limited to the whole number,
wherein n is the number of classes of the drugs in the automatic three-dimensional warehouse, kiIs the number of i-th class of drugs, pijFrequency of warehousing for ith class jth drug, tijThe time it takes for the stacker to pick the ith class jth drug, (x)ij，yij，zij) For the optimized location, v, of the ith class of jth drugyIn order to realize the uniform transportation speed of the conveyor belt,
Figure FDA0003102090430000033
the optimized positions of the medicines with the strongest degree of association with other medicines in the class i, r and w are the distance between the roadways and the width of the cargo space respectively,
Figure FDA0003102090430000034
a, B, C is the maximum number of columns, rows and layers of the warehouse, respectively, for the average location of all the drugs.
4. The method for optimizing the cargo space of the automated stereoscopic warehouse of the pharmaceutical enterprise according to claim 3, wherein solving the multi-objective cargo space optimization mathematical model specifically comprises:
and solving the multi-target cargo space optimization mathematical model by adopting an NSGA-II algorithm.
5. A system for optimizing a cargo space of an automated stereoscopic warehouse of a pharmaceutical enterprise, the system comprising:
memory, processor and computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of the preceding claims 1 to 4 are implemented when the computer program is executed by the processor.
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