CN103559396A - Automatic pharmacy storage location optimizing method based on improved chaos particle swarm algorithm - Google Patents
Automatic pharmacy storage location optimizing method based on improved chaos particle swarm algorithm Download PDFInfo
- Publication number
- CN103559396A CN103559396A CN201310532385.1A CN201310532385A CN103559396A CN 103559396 A CN103559396 A CN 103559396A CN 201310532385 A CN201310532385 A CN 201310532385A CN 103559396 A CN103559396 A CN 103559396A
- Authority
- CN
- China
- Prior art keywords
- msub
- mrow
- storage
- particle
- msubsup
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000003860 storage Methods 0.000 title claims abstract description 142
- 239000002245 particle Substances 0.000 title claims abstract description 76
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 title claims abstract description 30
- 239000003814 drug Substances 0.000 claims abstract description 79
- 229940079593 drug Drugs 0.000 claims abstract description 27
- 238000004364 calculation method Methods 0.000 claims abstract description 8
- 230000000739 chaotic effect Effects 0.000 claims description 29
- 238000005457 optimization Methods 0.000 claims description 27
- 238000007599 discharging Methods 0.000 claims description 5
- 238000013507 mapping Methods 0.000 claims description 3
- 238000009826 distribution Methods 0.000 abstract description 10
- 230000008859 change Effects 0.000 description 6
- 230000002068 genetic effect Effects 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000007306 turnover Effects 0.000 description 4
- 238000005192 partition Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 230000022542 predatory behavior Effects 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012384 transportation and delivery Methods 0.000 description 1
Images
Landscapes
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention discloses an automatic pharmacy storage location optimizing method based on the improved chaos particle swarm algorithm. The method comprises the following steps of A establishing a pharmacy drug storeroom model; B, randomly initializing the position and speed of each particle in population; C, evaluating the adaptability of each particle in the initial population, and storing an overall optimal position and an individual optimal position; D, updating the speed and position of each particle, and adopting a self-adaptive inertia coefficient to update the weight; E, evaluating the adaptability of each particle after being updated, and storing the overall optimal position and individual optimal position; f, executing the chaos local search for the optimal particle in the population, and updating the overall optimal position and individual optimal position; G, judging whether a maximal iteration number is achieved or not, outputting a calculation result - the optimal distribution strategy of storage location if the maximal iteration number is achieved; and returning to the step D if the maximal iteration number is not achieved. By adopting the method, efficiency of a pharmacy intelligent storage system can be effectively improved, and the dense storage can be realized.
Description
Technical Field
The invention relates to a pharmacy storage allocation optimization method, in particular to an automatic pharmacy storage allocation optimization method based on an improved chaotic particle swarm algorithm.
Background
The working efficiency of the automatic intelligent medicine room storing and taking system mainly depends on the distribution strategy of the storage area and the storage position, the reasonable storage layout and the storage position distribution strategy can greatly improve the storage efficiency of the warehouse and reduce useless consumption, and the system is also an important guarantee for realizing modern management and improving functions of the pharmacy, and has important practical significance for shortening prescription processing time, reducing medicine carrying and storing cost, reducing the loss of medicines in the storage process and the carrying process and improving the pharmacy income.
At present, in the existing logistics environment, the storage planning needs a plurality of principles to be considered, such as the loading condition of a goods shelf, the turnover speed, the reliability, the working efficiency, the product correlation and the like. In different periods, the required variety, the required quantity and the required frequency of the articles may change greatly, the access rate and the relative lightness of the articles change, and the path and the time cost for accessing the articles on different storage positions are different, so that the storage positions of the articles should not be unchanged, and a plurality of storage positions should be exchanged periodically according to the stability of a shelf, the change of the required rate of the articles, the frequency of entering and exiting the warehouse and other factors, so as to ensure that the distribution of the storage positions is in a reasonable state.
At present, the research of scholars at home and abroad on the problem of storage allocation has achieved a lot of achievements: the research methods of the reservoir allocation problem of J.P.van den Berg and Zijm W.H.M are classified, and there are three main methods: class-based classification, stochastic classification, and partition classification. Byung Chun Park et al divide the reservoir area into secondary reservoir areas: the high turnover rate storage area is close to the warehouse-in/out table, and the low turnover rate storage area is far away from the warehouse-in/out table. Zhang Xiao Na and so on have proposed the distribution method of the coupling storage position of the empty and actual goods area based on database, realize the partition and distribution of the storage position through the database, not merely guarantee the high supplies of frequency of delivery out of the warehouse fast, more importantly while changing products and craft, can set up the corresponding empty and actual goods area automatically and fast according to new products or craft, the automatic coupling realizes the new flexible partition. The configuration problem of the reservoir inversion and storage positions of the frequently-shining Liu and Liu is solved by adopting a layering sequence method; hsieh and Tsai propose a BOM-oriented library bit allocation method based on classification; thonemann and Brandeau apply turnover and classification for library bit allocation in a random environment; muralidharan et al propose a new heuristic-based method combining random storage and classified storage for allocation; mansuri et al propose a calculation method for dynamic storage; poulos et al propose a new genetic algorithm of crossover operators to solve the problem of distribution of automatic warehouse replenishment storage objects; the genetic algorithm based on Pareto optimal solution is proposed by Liusain et al to solve the problem of the storage allocation scheduling strategy. Muppani and Adil and the like construct a nonlinear integer programming model and solve the model by a branch-and-bound method in consideration of minimization of AS/RS storage space and minimum picking operation cost, and research redistribution of classified storage; and (4) in the strict cloud, the idea of optimizing the position number of the storage is provided on the basis of analyzing various factors of the operation efficiency of the stacker. The plum-juan and the like establish a multi-objective model of the allocation optimization problem of the storage space of the fixed shelf system, and provide an improved genetic algorithm based on Pareto optimization and niche technology for solving the problem. Zhao Xuefeng and the like research the problem of automatic medicine storage position distribution in a medicine room, provide a multi-target storage position distribution model aiming at improving the warehouse-in and warehouse-out efficiency and the space utilization rate, and solve by adopting a two-stage genetic algorithm. Particle swarm optimization algorithms, originally proposed by Kenney and Eberhart in 1995, arose from studies on the predatory behavior of bird populations. The particle swarm algorithm has been widely used due to its simple concept, easy realization, and low requirement for optimization function. However, due to the lack of population diversity, the particle swarm algorithm is easy to generate the phenomenon of early maturity and is easy to fall into a local extreme point in the later stage of the algorithm.
Chaos is a general phenomenon in a nonlinear system, and has some special properties, for example, chaotic motion can not repeatedly traverse all states in a certain range according to itself, and the extremely weak change of the initial value condition can cause huge change of system behavior. Based on the two points, chaos and particle swarm optimization are combined, the provided improved algorithm can avoid the particle swarm algorithm from falling into a local solution, and a simulation result shows that the algorithm provided by the invention has better performance and can better solve the problem of optimizing the storage allocation of the automatic storage system.
Disclosure of Invention
The invention aims to provide an automatic pharmacy storage allocation optimization method based on an improved chaotic particle swarm algorithm, which can get rid of the defect that a basic particle swarm algorithm is easy to fall into a local extreme point in the later period, keeps the rapidity of the early-period search, effectively improves the efficiency of a pharmacy intelligent storage system, and realizes the dense storage.
The invention adopts the following technical scheme:
an automatic pharmacy storage allocation optimization method based on an improved chaotic particle swarm algorithm comprises the following steps:
a, establishing a pharmacy drug storage library model;
randomly initializing the position and speed of each particle in the population;
evaluating the fitness of each particle in the initial population, and storing the global optimal position gkAnd individual optimum position pk；
D, updating the speed and the position of each particle, and updating the weight by adopting a self-adaptive inertia coefficient;
evaluating the fitness of each particle after updating, and storing the global optimal position gkAnd aBody optimum position pk；
F, executing chaotic local search on the best particles in the population, namely the particles with the best fitness of 20 percent in the population, and updating the global optimal position gkAnd individual optimum position pk；
G, judging whether the maximum iteration number is reached, and if the maximum iteration number is reached, outputting a calculation result, namely an optimal allocation strategy of the medicine storage positions; if not, returning to the step D.
In the step A, m layers of n columns are arranged in the storage region medicine storage, the column closest to the medicine feeding port is marked as the 1 st column, the bottommost layer is marked as the 1 st layer, the storage position in the ith layer j column is marked as (i, j), (i =1, 2.. multidot.m; j =1, 2.. multidot.n), and the position of the medicine feeding port and the position of the medicine discharging port are marked as (0, 0); when each storage bit is coded, the storage bit positioned at the j layer of the ith column is coded into (i-1) m + j; when a certain storage position is stored in goods, the corresponding element in the array is marked as 1, and the storage position is not stored in the goods and is marked as 0; each storage position has the same length, but different width and height, and the same storage area has the same layer height; the storage area can store k kinds of medicines, and each storage position can only store one kind of medicine; obtaining a storage position layout model
The constraint formula is as follows:
hj∈{minhi，maxhi}；
xij∈{0，1}；
in the formula (f)jIndicates the frequency of use of the jth drug, hiHeight of the ith layer of the storage area, hjThe height of the jth medicine is represented, and the height of the medicine takes the minimum value of three overall dimensions of the medicine;
l represents a layer height of hiThe number of layers of (a); x is the number ofijIs a decision variable when xijH is indicated by "= 1 = hiThe jth medicine is stored in the layer height.
In the step D, an iterative equation of speed is utilized <math>
<mrow>
<msubsup>
<mi>v</mi>
<mi>id</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>=</mo>
<mi>w</mi>
<mo>×</mo>
<msubsup>
<mi>v</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>⊕</mo>
<msub>
<mi>c</mi>
<mn>1</mn>
</msub>
<mo>×</mo>
<msub>
<mi>r</mi>
<mn>1</mn>
</msub>
<mo>×</mo>
<mrow>
<mo>(</mo>
<msubsup>
<mi>p</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>⊕</mo>
<msub>
<mi>c</mi>
<mn>2</mn>
</msub>
<mo>×</mo>
<msub>
<mi>r</mi>
<mn>2</mn>
</msub>
<mo>×</mo>
<mrow>
<mo>(</mo>
<msubsup>
<mi>g</mi>
<mi>gd</mi>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
</mrow>
</math> Updating the velocity of each particle;
using position iteration equations
Updating the position of each particle;
using formulas <math>
<mrow>
<mi>w</mi>
<mo>=</mo>
<mfenced open='{' close=''>
<mtable>
<mtr>
<mtd>
<msub>
<mi>w</mi>
<mi>min</mi>
</msub>
<mo>-</mo>
<mfrac>
<mrow>
<mrow>
<mo>(</mo>
<msub>
<mi>w</mi>
<mi>max</mi>
</msub>
<mo>-</mo>
<msub>
<mi>w</mi>
<mi>min</mi>
</msub>
<mo>)</mo>
</mrow>
<mrow>
<mo>(</mo>
<mi>f</mi>
<mo>-</mo>
<msub>
<mi>f</mi>
<mi>min</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>f</mi>
<mi>avg</mi>
</msub>
<mo>-</mo>
<msub>
<mi>f</mi>
<mi>min</mi>
</msub>
</mrow>
</mfrac>
<mo>,</mo>
<mi>f</mi>
<mo>≤</mo>
<msub>
<mi>f</mi>
<mi>avg</mi>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>w</mi>
<mi>max</mi>
</msub>
<mo>,</mo>
<mi>f</mi>
<mo>≥</mo>
<msub>
<mi>f</mi>
<mi>avg</mi>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</math> Updating inertial weights, wherein wmax、wminThe maximum and minimum values of w, respectively; f is the current objective function of the particle; f. ofavgAnd fminRespectively, the average target value and the minimum target value of all the particles at present.
In the step G, the optimal particles in the population are treated as followsThe column formula executes chaotic local search and updates the global optimal position gkAnd individual optimum position pk；
(1) Using e.g. the formula zj,k+1=μzj,k(1-zj,k),k=0,1,2,...,0≤zj,kGenerating a chaotic variable by Logistic mapping less than or equal to 1; wherein z isj,k，zj,k+1The chaotic variables obtained by the k and k +1 iterations respectively are in [ -1,1 [ ]]Interval, μ is the control variable, when μ =4,in time, Logistic is completely in a chaotic state;
（2）
wherein,
the current optimal solution is obtained; etajIs an adjustment factor;
(3) according to
To carry out etajAdaptive variation, where γ is the neighborhood radius, γ = 0.1; k is a radical ofmaxSetting the maximum iteration number for the algorithm; k is the current iteration number;
is the current optimal solution.
The chaotic algorithm and the particle swarm optimization algorithm are combined, and the chaotic motion is applied to the optimization search process by utilizing the characteristics of traversal type, randomness and the like of the chaotic motion. When the particles fall into premature convergence, jumping out local optimum by chaotic disturbance, and quickly searching an optimum solution, thereby improving the precision and the convergence speed of the solution; meanwhile, the self-adaptive inertia coefficient w is adopted, the size of the self-adaptive inertia coefficient w is adjusted to change the strength of the searching capability, a larger w value is beneficial to improving the convergence speed of the algorithm, and a smaller w value can improve the accuracy of the algorithm. The invention not only accelerates the convergence speed, but also gets rid of the defect that the basic particle swarm algorithm is easy to fall into a local extreme point at the later stage, utilizes the existing historical information to fly to the global optimal solution, effectively improves the efficiency of the intelligent storage system of the pharmacy and realizes the intensive storage.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
As shown in fig. 1, the automated pharmacy storage allocation optimization method based on the improved chaotic particle swarm algorithm of the present invention includes the following steps:
a, establishing a pharmacy drug storage library model; setting m layers of n columns in the storage region drug storage, recording the column closest to the drug loading port as the 1 st column, recording the bottommost layer as the 1 st layer, recording the storage position in the ith layer j column as (i, j), (i =1,2,.. multidot.m; j =1,2,. multidot.n), and recording the position of entering and exiting the drug storage port as (0, 0); when each storage bit is coded, the storage bit positioned at the j layer of the ith column is coded into (i-1) m + j; arranging the storage bit codes from small to large to form a one-dimensional array; when a certain storage position is stored in goods, the corresponding element in the array is marked as 1, and the storage position is not stored in the goods and is marked as 0; each storage position has the same length, but different width and height, and the same storage area has the same layer height; neglecting the medicine storage time; the total weight of the goods stored on the goods shelf does not exceed the bearing capacity of the goods shelf; the storage area can store s kinds of medicines, and each storage position can only store one kind of medicine; obtaining a storage position layout model
The constraint formula is as follows:
hj∈{minhi,maxhi};---(2)
in the formula (f)jIndicates the frequency of use of the jth drug, hiHeight of the ith layer of the storage area, hjThe height of the jth medicine is represented, and the height of the medicine takes the minimum value of three overall dimensions of the medicine; l represents a layer height of hiThe number of layers of (a); x is the number ofijFor decision variables, when xijH is indicated by "= 1 = hiThe jth medicine is stored in the layer height.
The formula (1) shows that the smaller the height difference between the medicine storage area and the medicines is, the more densely the medicines are placed; the constraint formula (2) shows that the sum of the layer heights of all the medicine storage layers does not exceed the effective storage height; the constraint formula (3) shows that when the height of the medicine storage layer is determined, all medicines meeting the height requirement of the layer can be placed in the area as much as possible, one area stores medicines with a certain height, the medicines with a certain height cannot be placed in other areas after being placed in the certain area, and the medicines with other height ranges cannot be placed after the medicines with a certain height are placed in the certain area.
Randomly initializing the position and speed of each particle in the population;
in this embodiment, X is a mode of storage of the article, i.e., the location of the particles; v is the displacement of two different storage modes, namely the velocity of the particles. Using the total number of storage bits mxn as the search space dimension of particle group, and setting V1And V2Is a replacement set of two different storage modes of the article, V1And V2Is denoted as V1⊕V2Obtaining the iterative equation of the ith particle velocity <math>
<mrow>
<msubsup>
<mi>v</mi>
<mi>id</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>=</mo>
<mi>w</mi>
<mo>×</mo>
<msubsup>
<mi>v</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>⊕</mo>
<msub>
<mi>c</mi>
<mn>1</mn>
</msub>
<mo>×</mo>
<msub>
<mi>r</mi>
<mn>1</mn>
</msub>
<mo>×</mo>
<mrow>
<mo>(</mo>
<msubsup>
<mi>p</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>⊕</mo>
<msub>
<mi>c</mi>
<mn>2</mn>
</msub>
<mo>×</mo>
<msub>
<mi>r</mi>
<mn>2</mn>
</msub>
<mo>×</mo>
<mrow>
<mo>(</mo>
<msubsup>
<mi>g</mi>
<mi>gd</mi>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>.</mo>
</mrow>
</math> Wherein v iskIs the velocity vector of the particle; x is the number ofkIs the position of the current particle; p is a radical ofkThe location of the optimal solution found for the particle itself; gkThe position of the optimal solution currently found for the whole population; r is1、r2A pseudo random number between 0 and 1; w is the inertial weight, c1、c2Is an acceleration constant; k is the number of iterations.
The addition of the position X and the speed V is recorded as X ^ V, which indicates that the replacement in V is acted on the position X, namely the storage position needing replacement is acted on the storage mode X, the result is still a storage mode of the article, and the position iterative equation for obtaining the storage position optimization problem based on the particle swarm optimization is shown as
The speed iteration of the conventional particle swarm algorithm comprises w and c1、c2And rand ()4 factors. In thatIn the storage allocation optimization problem, the speed is used for exchanging the storage state of a corresponding storage in two storage modes,
representing the best storage mode of the user and the replacement of the storage mode of the current item,
representing the best storage mode of the whole group and the replacement of the current storage mode of the articles. In the conventional particle swarm optimization, w, c1, c2 and rand ()4 factors are real numbers, and the meaning of the product of one real number and one permutation is difficult to determine, so the conventional optimization takes the factor as 1. In the calculation of the above-mentioned formula,
all represent a set of permutations, with the priorities of the three going from small to large: <math>
<mrow>
<mi>w</mi>
<mo>×</mo>
<msubsup>
<mi>v</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>,</mo>
</mrow>
</math> <math>
<mrow>
<msub>
<mi>c</mi>
<mn>1</mn>
</msub>
<mo>×</mo>
<msub>
<mi>r</mi>
<mn>1</mn>
</msub>
<mo>×</mo>
<mrow>
<mo>(</mo>
<msubsup>
<mi>p</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
</math> <math>
<mrow>
<msub>
<mi>c</mi>
<mn>2</mn>
</msub>
<mo>×</mo>
<msub>
<mi>r</mi>
<mn>2</mn>
</msub>
<mo>×</mo>
<mrow>
<mo>(</mo>
<msubsup>
<mi>g</mi>
<mi>gd</mi>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>.</mo>
</mrow>
</math> according to the comparison results of the three items, the particle swarm will keep the item different from the compared storage state when the speed updating is carried out.
Evaluating the fitness of each particle in the initial population, and storing the global optimal position gkAnd individual optimum position pk；
Calculating an objective function value (i.e., fitness) of each particle, and storing the position of the current particle and the calculated objective value in p of each particlekIn (1), all p arekThe position of the individual with the optimal target value and the target value are stored in gkIn (1).
D, updating the speed and the position of each particle through a speed iteration equation and a position iteration equation respectively, and updating the weight by adopting a formula (4) self-adaptive inertia coefficient;
as known from the velocity iterative equation, the value of the inertia weight w has an important influence on the convergence of the algorithm, if a larger value is taken, the local optimum is favorably jumped out, and a smaller value of w is favorably accelerated. The invention adopts an inertia weight method which is self-adaptively adjusted according to the population, and the weight is valued as <math>
<mrow>
<mi>w</mi>
<mo>=</mo>
<mfenced open='{' close=''>
<mtable>
<mtr>
<mtd>
<msub>
<mi>w</mi>
<mi>min</mi>
</msub>
<mo>-</mo>
<mfrac>
<mrow>
<mrow>
<mo>(</mo>
<msub>
<mi>w</mi>
<mi>max</mi>
</msub>
<mo>-</mo>
<msub>
<mi>w</mi>
<mi>min</mi>
</msub>
<mo>)</mo>
</mrow>
<mrow>
<mo>(</mo>
<mi>f</mi>
<mo>-</mo>
<msub>
<mi>f</mi>
<mi>min</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>f</mi>
<mi>avg</mi>
</msub>
<mo>-</mo>
<msub>
<mi>f</mi>
<mi>min</mi>
</msub>
</mrow>
</mfrac>
<mo>,</mo>
<mi>f</mi>
<mo>≤</mo>
<msub>
<mi>f</mi>
<mi>avg</mi>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>w</mi>
<mi>max</mi>
</msub>
<mo>,</mo>
<mi>f</mi>
<mo>≥</mo>
<msub>
<mi>f</mi>
<mi>avg</mi>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
</math>
Wherein, wmax、wminThe maximum and minimum values of w, respectively; f is the current objective function of the particle; f. ofavgAnd fminRespectively, the average target value and the minimum target value of all the particles at present.
Evaluating the fitness of each particle after updating, and storing the global optimal position gkAnd individual optimum position pk；
Calculating the value of the objective function of each particle, and comparing the current objective value of each particle in the population with pkIf the current target value is more optimal, the current position of the particle and the target value are more optimalNew compound pk. Comparing the current target value of each particle, and comparing all pkThe position of the individual with the optimal target value and the target value are stored in gkIn (1).
F, executing chaotic local search for the best particles in the population, namely the particles with the best fitness of 20 percent in the population according to the following formula, and updating the global optimal position gkAnd individual optimum position pk；
(1) In the chaotic particle swarm optimization algorithm, a formula z is selected and utilizedj,k+1=μzj,k(1-zj,k),k=0,1,2,...,0≤zj,kGenerating a chaotic variable by Logistic mapping less than or equal to 1; wherein z isj,k，zj,k+1The chaotic variables obtained by the k and k +1 iterations respectively are in [ -1,1 [ ]]Interval, μ is the control variable, when μ =4,
logistic is completely in a chaotic state;
（2）
wherein,
the current optimal solution is obtained; etajIs an adjustment factor;
(3) in the early stage of the search, it is desirable that the variables vary widely in order to jump out local extrema, ηjThe value should be larger; while the variable approaches the optimum value, η, as the search progressesjThe value should also gradually decrease. Therefore, in the present invention, according toTo carry out etajAdaptive variation, where γ is the neighborhood radius, γ = 0.1; k is a radical ofmaxSetting the maximum iteration number for the algorithm; k is the current iteration number;
is the current optimal solution.
G, judging whether the maximum iteration number is reached, and if the maximum iteration number is reached, outputting a calculation result, namely an optimal allocation strategy of the medicine storage positions; if not, returning to the step D.
The optimization method is used for the storage position configuration in a rapid medicine dispensing system of a certain domestic large hospital. In order to ensure that the medicines are smoothly delivered into and delivered out of the warehouse, the gap between the medicine storage groove and the medicine box must be kept within a certain range. The reasonable distribution and optimization medicine storage groove can effectively improve the efficiency of the medicine entering and exiting the warehouse, save the cost, improve the space utilization rate, facilitate the processing and the installation and improve the system reliability. In the quick medicine discharging system, the medicine storage cabinet is formed by arranging medicine storage grooves with the same length, different widths and different heights in a matrix manner. The height of the medicine storage grooves on the same layer is the same, the width of the medicine storage grooves can be different, and the height of the medicine storage grooves on different layers can be different. According to the information list of nearly 300 medicines in half a year in the hospital outpatient pharmacy, the statistics of the sizes of the medicine boxes shows that the length of the medicine box is within the range of 41 mm-200 mm, the width is within the range of 31 mm-150 mm, and the height is within the range of 12 mm-60 mm. Meanwhile, the floor area of the rapid medicine discharging system is required to be less than 20 square meters, and therefore the external dimension (length, width and height) of the medicine storage cabinet is determined as follows: 3500mm 1600mm 1800 mm.
The shelf of the quick medicine discharging system adopts a fixed shelf, 15 layers in total and 43 columns in maximum, and 645 storage positions in total. For the convenience of calculation, the storage bit codes are abstracted into a number sequence and are sorted according to a method of coding the storage shelves from a lower layer to a higher layer, and the storage bit codes are shown in table 1.
TABLE 1 bit-storing coding table
Through the analysis of 300 medicines, the medicines are classified into A, B, C, D types, and the method for accurately configuring the storage positions by using the storage position optimization model on the basis of classified storage is mainly used for managing the A type medicines according to the judgment of the dispensing requirement condition of the hospital pharmacy. For class B drugs we select class B based on categorical location storage. C. The number of the D-type goods is large, but the warehousing-in and warehousing-out frequency and the number are not large, and the random storage method can be adopted for management so as to achieve the purpose of saving the storage space. Because medicine weight is lighter, and the goods shelves of design satisfy the bearing requirement completely, do not consider the problem of storage position bearing. The required storage number and the average frequency of warehousing and warehousing of the four types of goods meeting the calculation of A, B, C and D are shown in the table 2.
TABLE 2 medicine Classification information Table
Through analysis of algorithm results, for the arrangement problem of the medicine storage positions, if a real optimal solution is obtained under the condition that a plurality of limiting conditions are met, the method cannot be realized. The storage bit arrangement obtained by the particle swarm optimization is a suboptimal solution, but plays a very positive role in improving the system operation efficiency and the utilization rate of the storage space.
Claims (4)
1. An automatic pharmacy storage allocation optimization method based on an improved chaotic particle swarm algorithm is characterized by comprising the following steps of:
a, establishing a pharmacy drug storage library model;
randomly initializing the position and speed of each particle in the population;
evaluating the fitness of each particle in the initial population, and storing the global optimal position gkAnd individual optimum position pk；
D, updating the speed and the position of each particle, and updating the weight by adopting a self-adaptive inertia coefficient;
evaluating the fitness of each particle after updating, and storing the global optimal position gkAnd individual optimum position pk；
F, executing chaotic local search on the best particles in the population, namely the particles with the best fitness of 20 percent in the population, and updating the global optimal position gkAnd individual optimum position pk；
G, judging whether the maximum iteration number is reached, and if the maximum iteration number is reached, outputting a calculation result, namely an optimal allocation strategy of the medicine storage positions; if not, returning to the step D.
2. The automated pharmacy storage space allocation optimization method based on the improved chaotic particle swarm algorithm according to claim 1, characterized in that: in the step A, m layers of n columns are arranged in the storage region medicine storage, the column closest to the medicine feeding port is marked as the 1 st column, the bottommost layer is marked as the 1 st layer, the storage position in the ith layer j column is marked as (i, j), (i =1, 2.. multidot.m; j =1, 2.. multidot.n), and the position of the medicine feeding port and the position of the medicine discharging port are marked as (0, 0); when each storage bit is coded, the storage bit positioned at the j layer of the ith column is coded into (i-1) m + j; when a certain storage position is stored in goods, the corresponding element in the array is marked as 1, and the storage position is not stored in the goods and is marked as 0; each storage position has the same length, but different width and height, and the same storage area has the same layer height; the storage area can store k kinds of medicines, and each storage position can only store one kind of medicine; obtaining a storage position layout model
The constraint formula is as follows:
hj∈{minhi，maxhi}；
xij∈{0，1}；
in the formula (f)jIndicates the frequency of use of the jth drug, hiHeight of the ith layer of the storage area, hjIndicating the height of the jth medicine, and taking the medicine from the height of the jth medicineThe smallest of the three overall dimensions of the article;
l represents a layer height of hiThe number of layers of (a); x is the number ofijIs a decision variable when xijH is indicated by "= 1 = hiThe jth medicine is stored in the layer height.
3. The automated pharmacy storage space allocation optimization method based on the improved chaotic particle swarm algorithm according to claim 2, characterized in that: in the step D, an iterative equation of speed is utilized <math>
<mrow>
<msubsup>
<mi>v</mi>
<mi>id</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>=</mo>
<mi>w</mi>
<mo>×</mo>
<msubsup>
<mi>v</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>⊕</mo>
<msub>
<mi>c</mi>
<mn>1</mn>
</msub>
<mo>×</mo>
<msub>
<mi>r</mi>
<mn>1</mn>
</msub>
<mo>×</mo>
<mrow>
<mo>(</mo>
<msubsup>
<mi>p</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>⊕</mo>
<msub>
<mi>c</mi>
<mn>2</mn>
</msub>
<mo>×</mo>
<msub>
<mi>r</mi>
<mn>2</mn>
</msub>
<mo>×</mo>
<mrow>
<mo>(</mo>
<msubsup>
<mi>g</mi>
<mi>gd</mi>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>id</mi>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
</mrow>
</math> Updating the velocity of each particle;
using position iteration equationsUpdating the position of each particle;
using formulas <math>
<mrow>
<mi>w</mi>
<mo>=</mo>
<mfenced open='{' close=''>
<mtable>
<mtr>
<mtd>
<msub>
<mi>w</mi>
<mi>min</mi>
</msub>
<mo>-</mo>
<mfrac>
<mrow>
<mrow>
<mo>(</mo>
<msub>
<mi>w</mi>
<mi>max</mi>
</msub>
<mo>-</mo>
<msub>
<mi>w</mi>
<mi>min</mi>
</msub>
<mo>)</mo>
</mrow>
<mrow>
<mo>(</mo>
<mi>f</mi>
<mo>-</mo>
<msub>
<mi>f</mi>
<mi>min</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>f</mi>
<mi>avg</mi>
</msub>
<mo>-</mo>
<msub>
<mi>f</mi>
<mi>min</mi>
</msub>
</mrow>
</mfrac>
<mo>,</mo>
<mi>f</mi>
<mo>≤</mo>
<msub>
<mi>f</mi>
<mi>avg</mi>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>w</mi>
<mi>max</mi>
</msub>
<mo>,</mo>
<mi>f</mi>
<mo>≥</mo>
<msub>
<mi>f</mi>
<mi>avg</mi>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</math> Updating inertial weights, wherein wmax、wminThe maximum and minimum values of w, respectively; f is the current objective function of the particle; f. ofavgAnd fminRespectively, the average target value and the minimum target value of all the particles at present.
4. The automated pharmacy storage space allocation optimization method based on the improved chaotic particle swarm algorithm according to claim 3, characterized in that: in the step G, chaotic local search is carried out on the optimal particles in the population according to the following formula, and the global optimal position G is updatedkAnd individual optimum position pk；
(1) Using e.g. the formula zj，k+1＝μzj，k(1-zj，k)，k＝0，1，2，...，0≤zj，kGenerating a chaotic variable by Logistic mapping less than or equal to 1; wherein z isj,k，zj,k+1The chaotic variables obtained by the k and k +1 iterations respectively are in [ -1,1 [ ]]Interval, μ is the control variable, when μ =4,in time, Logistic is completely in a chaotic state;
（2）
wherein,
the current optimal solution is obtained; etajIs an adjustment factor;
(3) according toTo carry out etajAdaptive variation, where γ is the neighborhood radius, γ = 0.1; k is a radical ofmaxSetting the maximum iteration number for the algorithm; k is the current iteration number;
is the current optimal solution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310532385.1A CN103559396B (en) | 2013-10-31 | 2013-10-31 | Based on the automatic dispensary stock's allocation optimization method improving chaos particle cluster algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310532385.1A CN103559396B (en) | 2013-10-31 | 2013-10-31 | Based on the automatic dispensary stock's allocation optimization method improving chaos particle cluster algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103559396A true CN103559396A (en) | 2014-02-05 |
| CN103559396B CN103559396B (en) | 2016-06-01 |
Family
ID=50013642
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310532385.1A Expired - Fee Related CN103559396B (en) | 2013-10-31 | 2013-10-31 | Based on the automatic dispensary stock's allocation optimization method improving chaos particle cluster algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103559396B (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104268640A (en) * | 2014-09-12 | 2015-01-07 | 河海大学常州校区 | Pilot designating method |
| CN105046378A (en) * | 2015-09-08 | 2015-11-11 | 电子科技大学 | Operation scheduling method based on seismic data |
| CN105844369A (en) * | 2016-04-18 | 2016-08-10 | 东南大学 | Pulverizing system optimal distribution method based on self-adaptive chaos particle swarm |
| CN105976054A (en) * | 2016-04-29 | 2016-09-28 | 国家电网公司 | Measuring instrument storage system goods location optimization method |
| CN106934577A (en) * | 2017-03-10 | 2017-07-07 | 北京京东尚科信息技术有限公司 | Goods layout method and device |
| CN107464013A (en) * | 2017-07-13 | 2017-12-12 | 南昌大学 | One kind is applied to the non-traditional layout warehouse aisles Optimization Design of V-type |
| CN107808215A (en) * | 2017-10-23 | 2018-03-16 | 南昌大学 | A kind of goods yard distribution optimization method applied to the non-traditional layout warehouse of Flying V-types |
| CN107944616A (en) * | 2017-11-20 | 2018-04-20 | 南昌大学 | A kind of goods yard distribution method of fish bone well tiered warehouse facility |
| CN108550007A (en) * | 2018-04-04 | 2018-09-18 | 中南大学 | A kind of slotting optimization method and system of pharmacy corporation automatic stereowarehouse |
| CN108615070A (en) * | 2018-04-30 | 2018-10-02 | 国网四川省电力公司电力科学研究院 | A kind of TDOA and AOA hybrid locating methods based on Chaos particle swarm optimization algorithm |
| CN109269502A (en) * | 2018-09-19 | 2019-01-25 | 沈阳航空航天大学 | A kind of no-manned plane three-dimensional Route planner based on more stragetic innovation particle swarm algorithms |
| CN109325721A (en) * | 2018-10-10 | 2019-02-12 | 江苏电力信息技术有限公司 | A kind of undercarriage method on goods and materials based on intelligent analysis process |
| CN109408691A (en) * | 2018-09-19 | 2019-03-01 | 安徽均维信息科技有限公司 | A kind of automatic regulation management system of archive information |
| CN110007240A (en) * | 2019-04-26 | 2019-07-12 | 贵州大学 | A kind of lithium ion battery residual life prediction technique |
| CN111667084A (en) * | 2019-03-06 | 2020-09-15 | 北京京东尚科信息技术有限公司 | Shelf deployment method, device, system and computer-readable storage medium |
| CN112330120A (en) * | 2020-10-27 | 2021-02-05 | 深圳市海柔创新科技有限公司 | Library bit allocation method, device, equipment, system and storage medium |
| CN113435805A (en) * | 2020-03-23 | 2021-09-24 | 北京京东振世信息技术有限公司 | Article storage information determining method, device, equipment and storage medium |
| CN117566305A (en) * | 2023-12-25 | 2024-02-20 | 昆明理工大学 | Automatic dispensing machine storage allocation method based on sequential alternating heuristic algorithm |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001036655A2 (en) * | 1999-11-12 | 2001-05-25 | Eli Lilly And Company | Method for optimization of a medium for glycopeptide fermentation |
| JP2004046303A (en) * | 2002-07-09 | 2004-02-12 | Fuji Electric Holdings Co Ltd | Optimum structure design method of electromagnet |
| CN102646213A (en) * | 2012-02-24 | 2012-08-22 | 上海交通大学 | Detecting method of corrosion diagnosis system of grounding grid based on chaos particle swarm algorithm |
| CN103337861A (en) * | 2013-05-31 | 2013-10-02 | 上海交通大学 | Power distribution network reactive power optimization method based on gold chaotic ecological niche particle swarm algorithm |
-
2013
- 2013-10-31 CN CN201310532385.1A patent/CN103559396B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001036655A2 (en) * | 1999-11-12 | 2001-05-25 | Eli Lilly And Company | Method for optimization of a medium for glycopeptide fermentation |
| JP2004046303A (en) * | 2002-07-09 | 2004-02-12 | Fuji Electric Holdings Co Ltd | Optimum structure design method of electromagnet |
| CN102646213A (en) * | 2012-02-24 | 2012-08-22 | 上海交通大学 | Detecting method of corrosion diagnosis system of grounding grid based on chaos particle swarm algorithm |
| CN103337861A (en) * | 2013-05-31 | 2013-10-02 | 上海交通大学 | Power distribution network reactive power optimization method based on gold chaotic ecological niche particle swarm algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 唐贤伦: "混沌粒子群优化算法理论及应用", 《中国博士学位论文全文数据库信息科技辑》 * |
| 陈月婷等: "基于改进粒子群算法的立体仓库货位分配优化", 《计算机工程与应用》 * |
Cited By (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104268640B (en) * | 2014-09-12 | 2017-10-20 | 河海大学常州校区 | A kind of pilot's assigning method |
| CN104268640A (en) * | 2014-09-12 | 2015-01-07 | 河海大学常州校区 | Pilot designating method |
| CN105046378A (en) * | 2015-09-08 | 2015-11-11 | 电子科技大学 | Operation scheduling method based on seismic data |
| CN105844369A (en) * | 2016-04-18 | 2016-08-10 | 东南大学 | Pulverizing system optimal distribution method based on self-adaptive chaos particle swarm |
| CN105976054A (en) * | 2016-04-29 | 2016-09-28 | 国家电网公司 | Measuring instrument storage system goods location optimization method |
| CN106934577A (en) * | 2017-03-10 | 2017-07-07 | 北京京东尚科信息技术有限公司 | Goods layout method and device |
| CN106934577B (en) * | 2017-03-10 | 2021-06-29 | 北京京东乾石科技有限公司 | Cargo layout method and device |
| CN107464013A (en) * | 2017-07-13 | 2017-12-12 | 南昌大学 | One kind is applied to the non-traditional layout warehouse aisles Optimization Design of V-type |
| CN107808215A (en) * | 2017-10-23 | 2018-03-16 | 南昌大学 | A kind of goods yard distribution optimization method applied to the non-traditional layout warehouse of Flying V-types |
| CN107944616A (en) * | 2017-11-20 | 2018-04-20 | 南昌大学 | A kind of goods yard distribution method of fish bone well tiered warehouse facility |
| CN108550007A (en) * | 2018-04-04 | 2018-09-18 | 中南大学 | A kind of slotting optimization method and system of pharmacy corporation automatic stereowarehouse |
| CN108550007B (en) * | 2018-04-04 | 2021-09-28 | 中南大学 | Goods space optimization method and system for automatic stereoscopic warehouse of pharmaceutical enterprise |
| CN108615070A (en) * | 2018-04-30 | 2018-10-02 | 国网四川省电力公司电力科学研究院 | A kind of TDOA and AOA hybrid locating methods based on Chaos particle swarm optimization algorithm |
| CN109269502A (en) * | 2018-09-19 | 2019-01-25 | 沈阳航空航天大学 | A kind of no-manned plane three-dimensional Route planner based on more stragetic innovation particle swarm algorithms |
| CN109408691A (en) * | 2018-09-19 | 2019-03-01 | 安徽均维信息科技有限公司 | A kind of automatic regulation management system of archive information |
| CN109408691B (en) * | 2018-09-19 | 2021-10-15 | 安徽均维信息科技有限公司 | Automatic regulation and control management system for archive information |
| CN109325721A (en) * | 2018-10-10 | 2019-02-12 | 江苏电力信息技术有限公司 | A kind of undercarriage method on goods and materials based on intelligent analysis process |
| CN111667084A (en) * | 2019-03-06 | 2020-09-15 | 北京京东尚科信息技术有限公司 | Shelf deployment method, device, system and computer-readable storage medium |
| CN110007240A (en) * | 2019-04-26 | 2019-07-12 | 贵州大学 | A kind of lithium ion battery residual life prediction technique |
| CN113435805A (en) * | 2020-03-23 | 2021-09-24 | 北京京东振世信息技术有限公司 | Article storage information determining method, device, equipment and storage medium |
| CN113435805B (en) * | 2020-03-23 | 2024-04-16 | 北京京东振世信息技术有限公司 | Method, device, equipment and storage medium for determining article storage information |
| CN112330120A (en) * | 2020-10-27 | 2021-02-05 | 深圳市海柔创新科技有限公司 | Library bit allocation method, device, equipment, system and storage medium |
| CN117566305A (en) * | 2023-12-25 | 2024-02-20 | 昆明理工大学 | Automatic dispensing machine storage allocation method based on sequential alternating heuristic algorithm |
| CN117566305B (en) * | 2023-12-25 | 2024-08-13 | 昆明理工大学 | Automatic dispensing machine storage allocation method based on sequential alternating heuristic algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103559396B (en) | 2016-06-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103559396B (en) | Based on the automatic dispensary stock's allocation optimization method improving chaos particle cluster algorithm | |
| CN113222293B (en) | Intelligent stereoscopic warehouse optimal scheduling method | |
| CN110909930B (en) | Goods position distribution method of mobile goods shelf storage system for refrigeration house | |
| CN108550007B (en) | Goods space optimization method and system for automatic stereoscopic warehouse of pharmaceutical enterprise | |
| Mirzaei et al. | The impact of integrated cluster-based storage allocation on parts-to-picker warehouse performance | |
| CN106779153B (en) | Intelligent stereoscopic warehouse goods space allocation optimization method | |
| Ene et al. | Storage location assignment and order picking optimization in the automotive industry | |
| CN109886478B (en) | Goods space optimization method for finished wine automatic stereoscopic warehouse | |
| CN110084545B (en) | Integrated scheduling method of multi-lane automatic stereoscopic warehouse based on mixed integer programming model | |
| Ekren et al. | Warehouse design under class-based storage policy of shuttle-based storage and retrieval system | |
| Bessenouci et al. | Metaheuristic based control of a flow rack automated storage retrieval system | |
| CN110980082A (en) | Automatic stereoscopic warehouse position allocation method | |
| CN107967586A (en) | A kind of power grid goods and materials storage optimization method | |
| CN113120487A (en) | Inventory system and goods storing and taking method | |
| CN116203959A (en) | Robot path planning method and system based on HAC algorithm | |
| CN115303689A (en) | Multi-roadway stereoscopic warehouse goods space allocation optimization method | |
| CN116596440A (en) | Automatic stereoscopic warehouse-in and warehouse-out intelligent scheduling method | |
| CN114781269B (en) | Three-dimensional warehouse optimization method based on cat swarm algorithm and three-dimensional warehouse | |
| CN114084568B (en) | Automatic cargo distribution method and system for intelligent storage rack | |
| Sari et al. | Design & modeling of a single machine flow rack AS/RS | |
| Wang et al. | Optimization of automated warehouse location based on genetic algorithm | |
| CN107464013A (en) | One kind is applied to the non-traditional layout warehouse aisles Optimization Design of V-type | |
| Kazemi et al. | A math-heuristic algorithm for concurrent assignment and sequence scheduling in multi-shuttle shared location automated storage and retrieval systems | |
| CN113762875A (en) | Article warehousing method and device and storage medium | |
| Sari | Performance evaluation of flow-rack and unit load automated storage & retrieval systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160601 Termination date: 20191031 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |