CN104573839A - Inventory management optimization method, device and system - Google Patents
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Abstract
The invention discloses an inventory management optimization method, device and system, wherein the inventory management optimization method comprises the following steps: setting the range of materials of each inventory and establishing a first-generation population according to the range of materials of each inventory; setting the dimensions of a variation sub-box; setting a terminating condition of inventory management optimization; performing cross-breeding according to individuals in the first-generation population, and selecting the individuals with highest fitness according to a preset selection number to participate in next-generation breeding after the end of cross-breeding of each generation; randomly selecting the individuals in each dimension of the variation sub-box as variation individuals according to the set dimensions of the variation sub-box when the breeding is determined to be in the trend of local convergence, performing cross-breeding on the variation individuals and the previous-generation individuals, and terminating the optimization when the breeding meets the termination condition of inventory management optimization. By adopting the technical scheme disclosed by the invention, an inventory optimal configuration scheme can be obtained within a relatively short period of time.
Description
Technical field
The present invention relates to inventory management techniques, be specifically related to a kind of stock control optimization method, device and system.
Background technology
Stock control is under the prerequisite ensureing enterprise's production, operation demand, and make tank farm stock often remain in rational level, grasp tank farm stock is dynamic, and timely and appropriate discovery proposes order, avoids overstocking or short supply, reduces inventory space and take, reduce stock total cost.How corresponding optimization problem carries out reasonable disposition to various resource complicated and changeable if being, thus under the prerequisite not affecting enterprise's normal activity, make warehouse cost reach minimum.
Compared with traditional stock control, existing stock control has numerous and diverse, circulation cycle fast, the ageing high of storage type of goods.One of solution that on-hand inventory management adopts is by information system of inventory control, adopts experience to configure adjust various weight to obtain the inventory rationing comparatively optimized by stock control personnel.
Two of the solution that on-hand inventory management adopts is combined with Intelligent Simulation System by traditional genetic algorithm, by the factors such as the tank farm stock of various material, consumption, circulation cycle, occupied ground size, marketing program are inputted computation model, the evolutionary process of algorithm is used to retain defect individual, to obtain optimized configuration.Wherein, so-called genetic algorithm refers to the computation model of the simulation natural selection of Darwinian evolutionism and the biological evolution process of genetic mechanisms, imitating the survival of the fittest in natural selection, the survival of the fittest, the biological heredity of the survival of the fittest and the rule of evolution, is a kind of method by simulating nature evolutionary process search optimum solution.
The solution of above-mentioned two kinds of stock controls has following weak point:
The method adopting experience to configure by stock control personnel is difficult to the configuration obtaining stock's optimum, and estimate very complicated, higher to the skill set requirements of stock control personnel, and owing to there is no intermediate result and state, be difficult to checking and adopt the method whether to reach allocation optimum.
By the method that traditional genetic algorithm is combined with Intelligent Simulation System, there is the phenomenons such as local search ability difference, Premature Convergence and random roam, cause convergence of algorithm performance poor, need just can find optimum solution for a long time, these shortcomings are more outstanding when inventory problem complicated and changeable, easily be absorbed in local optimum, more difficult acquisition allocation optimum scheme.Particularly, local peaking narrow and small in the optimum solution ranged space is more, be difficult to hit allocation optimum by the traditional genetic algorithm mode that random selecting variation is individual within the scope of sample space.
Summary of the invention
In view of this, fundamental purpose of the present invention is to provide a kind of stock control optimization method, device and system, can obtain the allocation optimum scheme of stock within a short period of time.
For achieving the above object, technical scheme of the present invention is achieved in that
The invention provides a kind of stock control optimization method, each supplies on hand scope is set, set up first generation population according to described each supplies on hand scope; Wherein, the individuality in described first generation population is the allocation plan of each supplies on hand; Arrange variation branch mailbox dimension, described variation branch mailbox dimension is the average interval number divided within the scope of each supplies on hand; Stock control is set and optimizes end condition;
Described method also comprises:
Carry out intersection breeding according to the individuality in described first generation population, and after per generation intersection breeding terminates, choose the highest individuality participation breeding of future generation of fitness by default quantity of choosing;
When determining that described breeding is tending towards local convergence, individual individual as variation according to the variation branch mailbox dimension arranged random selecting in each variation branch mailbox dimension, breed individual for described variation with previous generation individual the intersection, and when described breeding meets stock control optimization end condition, optimize and stop.
In such scheme, described first generation population of setting up described Revised genetic algorithum, comprising:
Based on chromosome coding, determine that often kind of supplies on hand corresponds to the regular coding position in described allocation plan, set up more than one and correspond to the individuality of regular coding position as first generation population using the quantity allocation plan of each supplies on hand.
In such scheme, described according to the individuality in described first generation population carry out intersection breeding, comprising:
By the supplies on hand of same-code position in each individuality configuration quantity independent assortment, the combined result that gains freedom intersects Breeding results as every generation.
In such scheme, described breeding is determined to be tending towards local convergence, comprising:
Continuous G
safter generation intersection breeding, special parameter variation range is less than particular value, be then defined as being tending towards local convergence.
In such scheme, the described variation branch mailbox dimension according to setting Stochastic choice in each variation branch mailbox dimension is individual individual as variation, comprising:
According to variation branch mailbox dimension Stochastic choice B in each variation branch mailbox dimension of setting
gindividual individual as variation; Wherein, B
gfor per generation adds the quantity of the individuality of variation, B
gfor population scale quantity P
swith dynamic variation factor B
pblong-pending.
In such scheme, describedly intersect in reproductive process by individual for described variation with previous generation is individual, described method also comprises: if branch mailbox region Q
toccur the individuality that configuration is more excellent, the inventory cost of acquisition is lower, then from described branch mailbox region Q
tthe variation individual amount that middle acquisition meets following expression adds reproductive process of intersecting of future generation:
g
s=P
s（G
l-G
t）/2G
l；
Wherein, g
sfor adding the variation individual amount added of reproductive process of future generation, G
lfor the total cost of a upper filial generation allocation optimum scheme, G
tfor the total cost of the allocation optimum scheme of current filial generation, P
sfor population scale quantity.
Present invention also offers a kind of stock control optimization device, described device comprises: arrange module, population foundation module, intersect and breed module and determination module; Wherein,
Described module is set, for arranging each supplies on hand scope; Arrange variation branch mailbox dimension, described variation branch mailbox dimension is the average interval number divided within the scope of each supplies on hand; Stock control is set and optimizes end condition;
Described population foundation module, for setting up first generation population according to described each supplies on hand scope; Wherein, the individuality in described first generation population is the allocation plan of each supplies on hand;
Described intersection breeds module, for carrying out intersection breeding according to the individuality in described first generation population, and after per generation intersection breeding terminates, chooses the highest individuality participation breeding of future generation of fitness by default quantity of choosing; Also for when described determination module determines that described breeding is tending towards local convergence, according to described variation branch mailbox dimension random selecting in each variation branch mailbox dimension individual conduct variation individuality arranging module installation, described variation individuality is intersected with previous generation individuality and breeds; Time also for determining that at described determination module described breeding meets stock control optimization end condition, optimizing and stopping;
Described determination module, for determining whether described breeding is tending towards local convergence; Also for determining whether described breeding meets stock control and optimize end condition.
In such scheme, described population foundation module, specifically for based on chromosome coding, determine that often kind of supplies on hand corresponds to the regular coding position in described allocation plan, set up more than one and correspond to the individuality of regular coding position as first generation population using the quantity allocation plan of each supplies on hand.
In such scheme, described intersection breeding module, specifically for by the supplies on hand of same-code position in each individuality configuration quantity independent assortment, the combined result that gains freedom intersects Breeding results as every generation.
In such scheme, described intersection breeding module, specifically also for the variation branch mailbox dimension Stochastic choice B in each variation branch mailbox dimension according to setting
gindividual individual as variation; Wherein, B
gfor per generation adds the quantity of the individuality of variation, B
gfor population scale quantity P
swith dynamic variation factor B
pblong-pending.
In such scheme, described intersection breeding module, if specifically also for there is the individuality that configuration is more excellent, the inventory cost of acquisition is lower, then in the next generation intersects reproductive process, add the variation individual amount meeting following expression:
g
s=P
s（G
l-G
t）/2G
l；
Wherein, g
sfor adding the variation individual amount added of reproductive process of future generation, G
lfor the total cost of a upper filial generation allocation optimum scheme, G
tfor the total cost of the allocation optimum scheme of current filial generation, P
sfor population scale quantity.
Present invention also offers a kind of stock control optimization system, described system comprises: stock control optimization device, stock control emulation module and interface display module; Wherein,
Described stock control optimization device comprises stock control optimization device of the present invention;
Described stock control emulation module, for inputting each supplies on hand scope; Input variation branch mailbox dimension;
Described interface display module, for showing the optimum results of described stock control optimization device, the supplies on hand allocation plan that display is optimum.
In such scheme, described system also comprises: data interface module and database module; Wherein,
Described data interface module, for described stock control emulation module to described stock control optimization device input data;
Described database module, for storing intermediate data and the optimum results of described stock control optimization device.
In such scheme, described system also comprises: optimal conditions arranges module and Optimal State control module; Wherein,
Described optimal conditions arranges module, optimizes end condition for arranging stock control;
Described Optimal State control module, for real-time monitoring system state, controls the startup of described stock control optimization device, operation, termination.
Stock control optimization method provided by the invention, device and system, arrange each supplies on hand scope, sets up first generation population according to described each supplies on hand scope; Wherein, the individuality in described first generation population is the allocation plan of each supplies on hand; Arrange variation branch mailbox dimension, described variation branch mailbox dimension is the average interval number divided within the scope of each supplies on hand; Stock control is set and optimizes end condition; Carry out intersection breeding according to the individuality in described first generation population, and after per generation intersection breeding terminates, choose the highest individuality participation breeding of future generation of fitness by default quantity of choosing; When determining that described breeding is tending towards local convergence, individual individual as variation according to the variation branch mailbox dimension arranged random selecting in each variation branch mailbox dimension, breed individual for described variation with previous generation individual the intersection, and when described breeding meets stock control optimization end condition, optimize and stop.Adopt technical scheme of the present invention, Revised genetic algorithum is combined with stock's simulation, data branch mailbox is combined with the choosing of variation value of genetic algorithm, effectively the comprehensive of population and diversity are ensured, avoid algorithm convergence in local optimum, the allocation optimum scheme of supplies on hand can be obtained in the short period of time.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of embodiment of the present invention stock control optimization method;
Fig. 2 is the composition structural representation of embodiment of the present invention stock control optimization device;
Fig. 3 is the composition structural representation of embodiment of the present invention stock control optimization system.
Embodiment
Below in conjunction with drawings and the specific embodiments, the present invention is further detailed explanation.
Fig. 1 is the schematic flow sheet of embodiment of the present invention stock control optimization method, as shown in Figure 1, comprises the following steps:
Step 101: arrange each supplies on hand scope, sets up first generation population according to described each supplies on hand scope; Wherein, the individuality in described first generation population is the allocation plan of each supplies on hand.
Here, described stock control optimization problem is the inventory rationing scheme under least cost, can expression formula (1) represent:
Minf
c=f（R
1，R
2，R
3，...，R
N） （1）
Wherein, f
cfor cost, R
i(i=1,2 ... N) be the allocation plan of each supplies on hand.
Wherein, described each supplies on hand scope comprises: material quantity, volume of material, streams rotary speed etc.; Be optimized for example with the stock control of three kinds of material configurations, the constraint expression formula of described three kinds of material scopes is:
V1=300；
V2=100；
V3=500；
100＜NUM1＜500；
210＜NUM2＜420；
1000＜NUM3＜2500；
40＜SPEED1＜100；
70＜SPEED2＜200；
50＜SPEED3＜300；
SPEED2＜SPEED3；
PCost=Rent×ADFactor；
Wherein, Vi(i=1,2,3) be each volume of material; NUMi(i=1,2,3) be each stock material quantity; SPEEDi(i=1,2,3) be each streams rotary speed; Rent is godown rent; ADFactor is godown rent adjustment factor; PCost is warehouse unit cost.
Wherein, Vi, NUMi, SPEEDi are the scope of each supplies on hand, corresponding to the individuality in described first generation population.
Preferably, described first generation population of setting up described Revised genetic algorithum, comprising:
Based on chromosome coding, determine that often kind of supplies on hand corresponds to the regular coding position in described allocation plan, set up more than one and correspond to the allocation plan of regular coding position as first generation population using the quantity allocation plan of each supplies on hand.
Here, be analogous to the chromosome coding in genetic algorithm, the individuality of the first generation population described in the embodiment of the present invention is the allocation plan of each supplies on hand, each material variety in described allocation plan is equivalent to the chromosome in described genetic algorithm, different types of material is encoded, set often kind of supplies on hand corresponding to the position in described individuality, such as, the value of described individuality is expressed as { Q
1, Q
2... Q
n, wherein N represents the kind of material, Q
nrepresent the quantitative range of N kind material, and the value of described individuality meets above-mentioned constraint condition.Such as: for the range constraint expression formula of above-mentioned three kinds of materials, arrange 1 for the first material, arrange 2 for the second material, arrange 3 for the third material, then Q
1for the quantity of the first material, Q
2for the quantity of the second material, Q
3for the quantity of the third material, under the constraint condition meeting above-mentioned three kinds of materials, first generation population can be set up according to the constraint condition of above-mentioned three kinds of materials, individuality in described first generation population can be expressed as { 150,250,1500}, { 200,300,1800} etc., can be multiple inventory rationing schemes including each material according to the range combinations of each material, described inventory rationing scheme be the individuality in described first generation population.
Step 102: variation branch mailbox dimension is set.
Here, described variation branch mailbox dimension d is the average interval number divided within the scope of each supplies on hand, which determines the granularity of branch mailbox, d is larger, then more to each stock's variable-value scope burst, branch mailbox granularity is thinner, not easily omit prioritization scheme, but assess the cost thereupon also can exponentially level increase.Setting population scale quantity is P
s, P
sfor the allocation plan individual amount that every generation produces, P
slarger, then per the individuality of generation breeding is more, produces defect individual possibility larger.According to the variation branch mailbox dimension d of setting, then the individuality of setting being divided into the Range Representation after d equal portions is Q
i/ d, wherein, i=1,2 ..., N.
Step 103: stock control is set and optimizes end condition.
Here, stock total cost G is set
m, G
mfor expecting the inventory cost reached, as the cost f that the allocation plan of certain generation in reproductive process obtains
c≤ G
mtime, illustrate that the allocation plan of current supplies on hand meets setting requirement, optimize and stop; The total algebraically G of setting breeding simultaneously
nif, breeding G
nstill f is not met after generation
c≤ G
m, then optimize termination, avoid being absorbed in unlimited endless loop.
Step 104: carry out intersection breeding according to the individuality in described first generation population, and after per generation breeding terminates, choose the highest individuality participation breeding of future generation of fitness by default quantity of choosing.
Here, described intersection of being carried out by individuality in described first generation population is bred, and comprising:
By the supplies on hand of same-code position in each individuality configuration quantity independent assortment, the combined result that gains freedom intersects Breeding results as every generation.
Concrete, described friendship breeding is equivalent to the chromosome independent assortment of the individuality in each population, refer in the allocation plan of supplies on hand described in each in embodiments of the present invention, when not changing the material code of each kind, each material is carried out independent assortment.Such as, for kind of the material allocation plan of three in step 101, first generation population comprises the allocation plan of two kinds of suppliess on hand, wherein, the first allocation plan is expressed as { 150, 250, 1500}, the second allocation plan is { 200, 300, 1800}, these two kinds of allocation plans are intersected and breeds, can obtain second generation population is { 150, 300, 1500}, { 150, 250, 1800}, { 150, 300, 1800}, { 200, 250, 1800}, { 200, 250, 1500}, { 200, 300, six kinds of allocation plans such as 1500}, obtain cost f in described six kinds of allocation plans respectively
cvalue, described fitness is the highest is cost f
cminimum, according to the cost f obtained
cvalue arrange from low to high, after every generation breeding, choose quantity choose cost f by presetting
cthe allocation plan of minimum supplies on hand participates in breeding of future generation as defect individual, and such as presetting and choosing quantity is 50%, then, after every generation breeding, choose cost f
cthe allocation plan of minimum front 50% supplies on hand participates in breeding of future generation as defect individual, then abandoned by the allocation plan of rear 50% supplies on hand.
Step 105: judge whether breeding is tending towards local convergence, when judged result is for being, performs step 106; When judged result is no, re-execute step 104.
Here, continuous G is set
sgeneration intersects breeds rear special parameter and described cost f
cvariation range is less than particular value, i.e. cost f described in special parameter
cchange in certain numerical value interval, then think that the allocation plan of supplies on hand of current acquisition reaches local optimum, be now defined as being tending towards local convergence; Wherein, described special parameter can be set as value at cost f
cdeng.
Step 106: individual as variation according to variation branch mailbox dimension Stochastic choice individuality in each variation branch mailbox dimension of setting, breeds individual for described variation with previous generation individual the intersection.
Here, for avoiding inventory rationing scheme in stock control optimization problem to be absorbed in local optimum, new allocation plan need be introduced and add reproductive process to provide excellent gene.Now introduce dynamic variation factor B
pbincrease mutation probability, wherein, described B
pbmeet formula (2):
B
pb=B
s+0.02×（G
c-G
s）（0＜B
pb≤0.5） （2）
In formula (2), B
pbfor the dynamic variation factor, B
sfor the static mutation probability of initial setting, G
sfor the unconverted reproductive order of generation of configuration scheme of setting, G
cthe unconverted reproductive order of generation of configuration scheme for reality.
Dynamic variation factor B
pbcan not becoming in iteration, convergence minimizing is individual to be introduced, and accelerating convergence process, increases for time unchanged the individual introducing that makes a variation at optimum prioritization scheme number simultaneously, introduces excellent genes, B
pbscope be defined as and be greater than 0 to be less than or equal to 0.5 1 aspects be in order to per generation introduces the dynamic variation factor, avoid the excellent genes of the excessive previous generation of causing of the dynamic variation factor too to be diluted on the other hand.Then often meet formula (3) for the individual amount adding variation:
B
g=P
s×B
pb（3）
In formula (3), B
gfor per generation adds the quantity of the individuality of variation, P
sfor population scale quantity, i.e. the allocation plan individual amount of every generation generation, B
pbfor the dynamic variation factor.
According to arranging variation branch mailbox dimension, the Range Representation after each individuality is divided into d equal portions is Q
i/ d, simultaneously in conjunction with supplies on hand kind N, is then equivalent to each supplies on hand span to be divided into d
nindividual set, corresponds to d
nindividual branch mailbox region.
For kind of the material of three described in step 101, the quantitative range of the first material is 100 < NUM1 < 500; The quantitative range of the second material is: 210 < NUM2 < 420; The quantitative range of the third material is: 1000 < NUM3 < 2500; Setting variation branch mailbox dimension d is 5, then the scope of the first material corresponding can be expressed as the subrange that 5 have 20 < NUM1 < 100 constraint conditions; The scope of corresponding the second material can be expressed as the subrange that 5 have 42 < NUM2 < 84 constraint conditions; The scope of the third material corresponding can be expressed as the subrange that 5 have 200 < NUM3 < 500 constraint conditions.So visible, variation branch mailbox dimension d is the average interval number divided within the scope of each supplies on hand, which determines the granularity of branch mailbox, d is larger, then more to each stock's variable-value scope burst, branch mailbox granularity is thinner, not easily omit prioritization scheme, but assess the cost thereupon also can exponentially level increase.
In each branch mailbox region, Stochastic choice is individual as the individual g of variation, joins optimization population and is combined into new allocation plan.The individual g of described variation meets formula (4):
g=Random（Q
1k，...Q
Nk，k=1,2，...d） （4）
Wherein, Q
nkfor the individuality in population, namely N kind material is in the quantity of kth dimension.
Individual for variation g and previous generation individuality is intersected in the process of breeding, as there is more excellent individual gt(t=1 in t filial generation ... n), the cost of acquisition is lower, then represent the branch mailbox region Q at its place
tthere is more excellent stock material allocation plan, then in reproductive process of future generation to Q
tmutagenic factor tilted, increase the proportion choosing material allocation plan in this branch mailbox region, be more conducive to expanding and choose individual proportion in excellent allocation plan interval.From described branch mailbox region Q
tthe variation individual amount that middle acquisition meets formula (5) adds reproductive process of intersecting of future generation:
g
s=P
s（G
l-G
t）/2G
l（5）
In formula (5), g
sfor adding the variation individual amount added of reproductive process of future generation, G
lfor the total cost of a upper filial generation allocation optimum scheme, G
tfor the total cost of the allocation optimum scheme of current filial generation, P
sfor population scale quantity, i.e. the allocation plan individual amount of every generation generation.
Step 107: judge whether that meeting stock control optimizes end condition, when judged result is for being, performs step 108; When judged result is no, re-execute step 104.
Here, described breeding meets stock control and optimizes end condition, comprising:
Pre-set stock's total cost and the total algebraically of breeding, when the stock's total cost pre-set described in the cost that the allocation plan of the supplies on hand in reproductive process obtains is not more than, determine that described breeding meets stock control and optimizes end condition; Or,
The total algebraically of breeding pre-set described in actual total algebraically of breeding reaches, and the cost that the allocation plan of supplies on hand in reproductive process obtains be greater than described in pre-set stock's total cost time, determine that described breeding meets stock control and optimizes end condition.
Concrete, described optimization end condition comprises two conditions, and first condition is the cost f obtained
c≤ G
m, wherein G
mfor stock's total cost of setting, when the cost that described inventory rationing scheme obtains is less than or equal to stock's total cost of setting, then illustrate that meeting stock control optimizes end condition, otherwise, proceed stock control and distribute rationally; Second condition is the total algebraically G of setting breeding
nif, breeding G
nstill f is not met after generation
c≤ G
m, illustrate that meeting stock control optimizes end condition.
Step 108: terminate to optimize.
Below in conjunction with specific embodiment, the present invention is further detailed explanation.
Table one is the detail list of the supplies on hand of the embodiment of the present invention, requires to be configured 5 to the 15 class commodity of stock, and to be 1.2,5# commodity must be greater than 2# commodity 2 times of storehouse quantity in storehouse quantity to godown rent adjustment factor.
| Trade name | Minimum inventory | The highest stock | Commodity volume | Average rate flow |
| 1# | 600 | 3000 | 20.1 | 200 |
| 2# | 550 | 2200 | 7.8 | 150 |
| 3# | 1500 | 1980 | 3.2 | 500 |
| 4# | 650 | 2240 | 17.3 | 210 |
| 5# | 1200 | 4160 | 4.3 | 400 |
| 6# | 720 | 1500 | 2.1 | 240 |
| 7# | 540 | 1780 | 1.3 | 180 |
| 8# | 120 | 320 | 6.7 | 40 |
| 9# | 90 | 490 | 10.1 | 30 |
| 10# | 1230 | 3000 | 2.2 | 410 |
| 11# | 1890 | 4100 | 1.7 | 630 |
| 12# | 300 | 900 | 3.4 | 100 |
| 13# | 360 | 800 | 8.2 | 120 |
| 14# | 660 | 1540 | 6.7 | 220 |
| 15# | 160 | 700 | 14.2 | 53 |
Table one
By comparing with now general traditional genetic algorithm, adopt the stock control optimization method of the embodiment of the present invention in the marketing stock control optimization of process business hall, optimization efficiency on average improves 23%, the probability obtaining more excellent configuration is lifted at more than 30%, particularly the successful when multiple-objection optimization.Table two is for adopting the result data table of the stock control optimization method of traditional genetic algorithm, table three is for adopting the result data table of the stock control optimization method of Revised genetic algorithum, by the optimum results to 5 same task, can find out in table three that calculated amount when reaching convergence comparatively significantly reduces in table two, and its error is also milder with the increase of stock kind.And analyzed by task 5 in his-and-hers watches two, itself and the allocation optimum reason that differs greatly is that allocation plan that it obtains is in comparatively within the scope of broad peak, crest slope is milder, and in actual optimum Xie Chu and narrow peak ranges, crest efficiency is precipitous, cause the parameter of employing table one until iterations full 400 generations do not obtain satisfactory solution yet, and adopt the Improving Genetic Algorithm in this programme to solve this problem preferably.
| Mission number | Stock kind | Reproductive order of generation during convergence | With allocation optimum difference |
| 1 | 5 | 12 | 0.23% |
| 2 | 7 | 21 | 5.61% |
| 3 | 9 | 56 | 9.42% |
| 4 | 11 | 94 | 12.37% |
| 5 | 15 | 400 | 18.22% |
Table two
| Mission number | Stock kind | Reproductive order of generation during convergence | With allocation optimum difference |
| 1 | 5 | 11 | 0.12% |
| 2 | 7 | 18 | 0.61% |
| 3 | 9 | 27 | 1.45% |
| 4 | 11 | 44 | 2.32% |
| 5 | 15 | 79 | 2.21% |
Table three
Fig. 2 is the composition structural representation of the stock control optimization device of the embodiment of the present invention, and as shown in Figure 2, described stock control optimization device 20 comprises: arrange module 21, population foundation module 22, intersect and breed module 23 and determination module 24; Wherein,
Described module 21 is set, for arranging each supplies on hand scope; For arranging variation branch mailbox dimension, described variation branch mailbox dimension is the average interval number divided within the scope of each supplies on hand; End condition is optimized for arranging stock control;
Described population foundation module 22, for setting up first generation population according to described each supplies on hand scope; Wherein, the individuality in described first generation population is the allocation plan of each supplies on hand;
Described intersection breeds module 23, for carrying out intersection breeding according to the individuality in described first generation population, and after per generation intersection breeding terminates, chooses the highest individuality participation breeding of future generation of fitness by default quantity of choosing; Also for when described determination module 24 determines that described breeding is tending towards local convergence, according to the described variation branch mailbox dimension random selecting individuality conduct variation individuality in each variation branch mailbox dimension arranging module 21 and arrange, breed individual for described variation with previous generation individual the intersection; Time also for determining that at described determination module 24 described breeding meets stock control optimization end condition, optimizing and stopping;
Described determination module 24, for determining whether described breeding is tending towards local convergence; Also for determining whether described breeding meets stock control and optimize end condition.
Described population foundation module 22, specifically for based on chromosome coding, determine that often kind of supplies on hand corresponds to the regular coding position in described allocation plan, set up more than one and correspond to the individuality of regular coding position as first generation population using the quantity allocation plan of each supplies on hand.
Described intersection breeding module 23, specifically for by the supplies on hand of same-code position in each individuality configuration quantity independent assortment, the combined result that gains freedom intersects Breeding results as every generation.
Described intersection breeding module 23, specifically also for the variation branch mailbox dimension Stochastic choice B in each variation branch mailbox dimension according to setting
gindividual individual as variation; Wherein, B
gmeet following expression:
B
g=P
s×B
pb；
Wherein, B
gfor per generation adds the quantity of the individuality of variation, P
sfor population scale quantity, B
pbfor the dynamic variation factor;
Wherein, described dynamic variation factor B
pbmeet following expression:
B
pb=B
s+0.02×（G
c-G
s）（0＜B
pb≤0.5）；
Wherein, B
pbfor the dynamic variation factor, B
sfor the static mutation probability of initial setting, G
sfor the unconverted reproductive order of generation of configuration scheme of setting, G
cthe unconverted reproductive order of generation of configuration scheme for reality.
Described intersection breeding module 23, if specifically also for there is the individuality that configuration is more excellent, the inventory cost of acquisition is lower, then in the next generation intersects reproductive process, add the variation individual amount meeting following expression:
g
s=P
s（G
l-G
t）/2G
l；
Wherein, g
sfor adding the variation individual amount added of reproductive process of future generation, G
lfor the total cost of a upper filial generation allocation optimum scheme, G
tfor the total cost of the allocation optimum scheme of current filial generation, P
sfor population scale quantity.
Wherein, described arrange module 21, population foundation module 22, intersect breeding module 23 and determination module 24 in actual applications, all can by the central processing unit (CPU in device, Central Processing Unit) or digital signal processor (DSP, Digital Signal Processor) or programmable logic array (FPGA, Field-Programmable Gate Array) realization.
It will be appreciated by those skilled in the art that the practical function of each processing module of the stock control optimum management device shown in Fig. 2 can refer to the associated description of aforesaid stock control optimization method and understands.It will be appreciated by those skilled in the art that the function of each module in the stock control optimum management device shown in Fig. 2 realizes by the program run on processor, also realize by concrete logical circuit.
Obviously, those skilled in the art should be understood that, above-mentioned of the present invention each module or each step can realize with general calculation element, they can concentrate on single calculation element, or be distributed on network that multiple calculation element forms, alternatively, they can realize with the executable program code of calculation element, thus, they can be stored and be performed by calculation element in the storage device, and in some cases, step shown or described by can performing with the order be different from herein, or they are made into each integrated circuit modules respectively, or the multiple module in them or step are made into single integrated circuit module to realize.Like this, the present invention is not restricted to any specific hardware and software combination.
Fig. 3 is the composition structural representation of the stock control optimization system of the embodiment of the present invention, and as shown in Figure 3, described system comprises: stock control optimization device 20, stock control emulation module 32 and interface display module 33; Wherein,
Described stock control optimization device 20 is the stock control optimization device shown in Fig. 2, for setting up algorithm model, when the algorithm model set up is Revised genetic algorithum model, carry out data processing according to described Revised genetic algorithum model, obtain optimum supplies on hand allocation plan; Describedly carry out data processing according to described Revised genetic algorithum model and comprise: each supplies on hand scope is set, sets up first generation population according to described each supplies on hand scope;
Wherein, the individuality in described first generation population is the allocation plan of each supplies on hand; Arrange variation branch mailbox dimension, described variation branch mailbox dimension is the average interval number divided within the scope of each supplies on hand; Stock control is set and optimizes end condition; Carry out intersection breeding according to the individuality in described first generation population, and after per generation intersection breeding terminates, choose the highest individuality participation breeding of future generation of fitness by default quantity of choosing; When determining that described breeding is tending towards local convergence, individual individual as variation according to the variation branch mailbox dimension arranged random selecting in each variation branch mailbox dimension, breed individual for described variation with previous generation individual the intersection, and when described breeding meets stock control optimization end condition, optimize and stop;
Described stock control emulation module 32, for inputting each supplies on hand scope; Input variation branch mailbox dimension;
Described interface display module 33, for showing the optimum results of described stock control optimization device 20, the supplies on hand allocation plan that display is optimum.
Here, described stock control optimization device 20 is the core of whole optimization system, is mainly used in the operation etc. of the choosing of optimized algorithm, optimized algorithm, is provided with change-over switch, for handover optimization algorithm in described stock control optimization device 20; Adopt the stock control optimization device 20 in the present embodiment, the stock control optimization method of the stock control optimization method of traditional genetic algorithm and Revised genetic algorithum can also be compared, obtain optimum supplies on hand allocation plan.
Described system also comprises: data interface module 34 and database module 35; Wherein,
Described data interface module 34, inputs data for described stock control emulation module 32 to described stock control optimization device 20;
Described database module 35, for storing intermediate data and the optimum results data of described stock control optimization device 20.
Described system also comprises: optimal conditions arranges module 36 and Optimal State control module 37; Wherein,
Described optimal conditions arranges module 36, optimizes end condition for arranging stock control;
Described Optimal State control module 37, for real-time monitoring system state, controls the startup of described stock control optimization device 20, operation, termination.
Wherein, described stock control optimization device 20, stock control emulation module 32, optimal conditions arrange module 36 and Optimal State control module 37 in actual applications, all can be realized by CPU or DSP in system or FPGA; Described data interface module 34 in actual applications, can by RS-232 interface, RS-232-C interface or RS-485 Interface realization; Described database module 35 in actual applications, can be realized by storer; Described interface display module 33 in actual applications, can be realized by display.
The above, be only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention.All any amendments done within the spirit and scope of the present invention, equivalent and improvement etc., be all included within protection scope of the present invention.
Claims (14)
1. a stock control optimization method, is characterized in that, arranges each supplies on hand scope, sets up first generation population according to described each supplies on hand scope; Wherein, the individuality in described first generation population is the allocation plan of each supplies on hand; Arrange variation branch mailbox dimension, described variation branch mailbox dimension is the average interval number divided within the scope of each supplies on hand; Stock control is set and optimizes end condition;
Described method also comprises:
Carry out intersection breeding according to the individuality in described first generation population, and after per generation intersection breeding terminates, choose the highest individuality participation breeding of future generation of fitness by default quantity of choosing;
When determining that described breeding is tending towards local convergence, individual individual as variation according to the variation branch mailbox dimension arranged random selecting in each variation branch mailbox dimension, breed individual for described variation with previous generation individual the intersection, and when described breeding meets stock control optimization end condition, optimize and stop.
2. method according to claim 1, is characterized in that, described first generation population of setting up described Revised genetic algorithum, comprising:
Based on chromosome coding, determine that often kind of supplies on hand corresponds to the regular coding position in described allocation plan, set up more than one and correspond to the individuality of regular coding position as first generation population using the quantity allocation plan of each supplies on hand.
3. method according to claim 1 and 2, is characterized in that, described according to the individuality in described first generation population carry out intersection breeding, comprising:
By the supplies on hand of same-code position in each individuality configuration quantity independent assortment, the combined result that gains freedom intersects Breeding results as every generation.
4. method according to claim 1, is characterized in that, described breeding is determined to be tending towards local convergence, comprising:
Continuous G
safter generation intersection breeding, special parameter variation range is less than particular value, be then defined as being tending towards local convergence.
5. the method according to claim 1 or 4, is characterized in that, the described variation branch mailbox dimension according to setting Stochastic choice in each variation branch mailbox dimension is individual individual as variation, comprising:
According to variation branch mailbox dimension Stochastic choice B in each variation branch mailbox dimension of setting
gindividual individual as variation; Wherein, B
gfor per generation adds the quantity of the individuality of variation, B
gfor population scale quantity P
swith dynamic variation factor B
pblong-pending.
6. method according to claim 1, is characterized in that, describedly intersects in reproductive process by individual for described variation with previous generation is individual, and described method also comprises: if branch mailbox region Q
toccur the individuality that configuration is more excellent, the inventory cost of acquisition is lower, then from described branch mailbox region Q
tthe variation individual amount that middle acquisition meets following expression adds reproductive process of intersecting of future generation:
g
s=P
s（G
l-G
t）/2G
l；
Wherein, g
sfor adding the variation individual amount added of reproductive process of future generation, G
lfor the total cost of a upper filial generation allocation optimum scheme, G
tfor the total cost of the allocation optimum scheme of current filial generation, P
sfor population scale quantity.
7. a stock control optimization device, is characterized in that, described device comprises: arrange module, population foundation module, intersect and breed module and determination module; Wherein,
Described module is set, for arranging each supplies on hand scope; Arrange variation branch mailbox dimension, described variation branch mailbox dimension is the average interval number divided within the scope of each supplies on hand; Stock control is set and optimizes end condition;
Described population foundation module, for setting up first generation population according to described each supplies on hand scope; Wherein, the individuality in described first generation population is the allocation plan of each supplies on hand;
Described intersection breeds module, for carrying out intersection breeding according to the individuality in described first generation population, and after per generation intersection breeding terminates, chooses the highest individuality participation breeding of future generation of fitness by default quantity of choosing; Also for when described determination module determines that described breeding is tending towards local convergence, according to described variation branch mailbox dimension random selecting in each variation branch mailbox dimension individual conduct variation individuality arranging module installation, described variation individuality is intersected with previous generation individuality and breeds; Time also for determining that at described determination module described breeding meets stock control optimization end condition, optimizing and stopping;
Described determination module, for determining whether described breeding is tending towards local convergence; Also for determining whether described breeding meets stock control and optimize end condition.
8. device according to claim 7, it is characterized in that, described population foundation module, specifically for based on chromosome coding, determine that often kind of supplies on hand corresponds to the regular coding position in described allocation plan, set up more than one and correspond to the individuality of regular coding position as first generation population using the quantity allocation plan of each supplies on hand.
9. the device according to claim 7 or 8, is characterized in that, described intersection breeding module, and specifically for by the supplies on hand of same-code position in each individuality configuration quantity independent assortment, the combined result that gains freedom intersects Breeding results as every generation.
10. device according to claim 7, is characterized in that, described intersection breeding module, specifically also for the variation branch mailbox dimension Stochastic choice B in each variation branch mailbox dimension according to setting
gindividual individual as variation; Wherein, B
gfor per generation adds the quantity of the individuality of variation, B
gfor population scale quantity P
swith dynamic variation factor B
pblong-pending.
11. devices according to claim 10, it is characterized in that, described intersection breeding module, if specifically also for there is the individuality that configuration is more excellent, the inventory cost obtained is lower, then in the next generation intersects reproductive process, add the variation individual amount meeting following expression:
g
s=P
s（G
l-G
t）/2G
l；
Wherein, g
sfor adding the variation individual amount added of reproductive process of future generation, G
lfor the total cost of a upper filial generation allocation optimum scheme, G
tfor the total cost of the allocation optimum scheme of current filial generation, P
sfor population scale quantity.
12. 1 kinds of stock control optimization system, is characterized in that, described system comprises: stock control optimization device, stock control emulation module and interface display module; Wherein,
Described stock control optimization device comprises the stock control optimization device described in any one of claim 7 to 11;
Described stock control emulation module, for inputting each supplies on hand scope; Input variation branch mailbox dimension;
Described interface display module, for showing the optimum results of described stock control optimization device, the supplies on hand allocation plan that display is optimum.
13. systems according to claim 12, is characterized in that, described system also comprises: data interface module and database module; Wherein,
Described data interface module, for described stock control emulation module to described stock control optimization device input data;
Described database module, for storing intermediate data and the optimum results of described stock control optimization device.
14. systems according to claim 12, is characterized in that, described system also comprises: optimal conditions arranges module and Optimal State control module; Wherein,
Described optimal conditions arranges module, optimizes end condition for arranging stock control;
Described Optimal State control module, for real-time monitoring system state, controls the startup of described stock control optimization device, operation, termination.
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