CN107451679A - A kind of production Order splitting cloud processing method based on immune algorithm - Google Patents
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Abstract
The invention provides a kind of production Order splitting cloud processing method based on immune algorithm, step 1：The production capacity of manufacturer is analyzed, by production capacity quantification, the production capacity of manufacturer is represented with specific mathematic(al) representation；Step 2：Data analysis is carried out to production order, according to different demands, extraction critical data represents a production order entirety, and the mathematical modeling of selection Optimal Production business is established based on critical data；Step 3：The mathematical modeling of the selection Optimal Production business is solved using immune algorithm.Method provided by the invention overcomes the deficiencies in the prior art, can be in the multiple different vendor's productions of customer selecting, reasonable distribution is carried out to production order, reached under conditions of meeting that the quality of production is qualified so that the effect that production cost is minimum, the time used is minimum；And the inventive method result of calculation is accurate, calculating speed is fast, is favorably improved production efficiency.
Description
Technical field
The present invention relates to production Order splitting, big data analysis or Computer Applied Technology field, more particularly to a kind of base
In the production Order splitting cloud processing method of immune algorithm.
Background technology
In modern manufacturing industry, it is larger to produce the amount of order, and client often requires that manufacturer under certain cost, in a short time
Produce the product for meeting quality standard.Single manufacturer is difficult to complete the larger order of output in a short time, promotes same district
The manufacturing enterprise for producing like product in domain cooperates, shared resource, the larger order of output is completed together, so that enterprise
Obtain more incomes.How quickly reasonably to give the larger Order splitting of output to different manufacturers, be manufacturing enterprise
The premise of cooperation and basis, and be also an interface cooperating between different enterprises, between increase member enterprise
Transparency plays an important role.When the multiple different vendors of customer selecting produce, how under conditions of meeting that the quality of production is qualified,
So that production cost is minimum, the time used is minimum, is the problem that those skilled in the art are directed to solving.
Immune algorithm (immune algorithm) is inspired by Immune System, is developed on the basis of Immunology
A kind of emerging intelligence computation method got up.Immune algorithm realizes the antigen recognizing similar to Immune System, cell
The function of differentiation, memory and self-control, has good systems response and independence, has to interference and relatively maintains system by force
Self-balancing ability；Produced using the diversity of immune system with support mechanism to keep the diversity of colony, overcome and typically seek
Intractable " precocity " problem, finally tries to achieve globally optimal solution during excellent process especially multi-modal function optimization.Immune algorithm
Regard a solution of model as an individual, the evaluation to individual obtains by calculating affinity, and individual selection is also
Carried out based on affinity.The affinity of individual is included between affinity and antibody and antibody between antibody and antigen
Affinity, it reflects the diversity of real immune system, therefore evaluation of the immune algorithm to individual is more comprehensive, its individual
Selection mode is also more reasonable, so immune algorithm is adapted to the solution that optimal solution is carried out to complex model.
Cloud treatment technology is one kind of distributed computing technology, its most basic concept, is by huge meter by network
Calculate processing routine and be split into numerous less subprogram automatically, then send the bulky systems being made up of multi-section server to and pass through
Calculate and result is returned into user after analyzing.By this technology, cloud treatment technology ISP can be in the several seconds
Within, reach processing number even hundred million information counted in terms of necessarily, reach and the network of " supercomputer " same powerful efficiency takes
Business.Cloud computing technology has been seen everywhere in web services, such as Search engine, network mailbox etc., and user simply enters
Simple instruction just can obtain bulk information.Data search, the function of analysis are not done in developing cloud computing only, following as analyzed
The bigger complex data computation analysis of the data volumes such as DNA structure, gene map sequencing, parsing cancer cell, can pass through this
Item technology is easily achievable.
This invention address that with reference to immune algorithm and cloud treatment technology, reasonable distribution is carried out to production order, reached full
Under conditions of the sufficient quality of production is qualified so that the effect that production cost is minimum, the time used is minimum.
The content of the invention
The technical problem to be solved in the present invention is in the multiple different vendor's productions of customer selecting, how production order is entered
Row reasonable distribution, reach under conditions of meeting that the quality of production is qualified so that the effect that production cost is minimum, the time used is minimum
Fruit.
In order to solve the above-mentioned technical problem, the technical scheme is that providing a kind of production order based on immune algorithm
Distribute cloud processing method, it is characterised in that：This method is made up of following 3 steps：
Step 1：The production capacity of manufacturer is analyzed, by production capacity quantification, represents raw with specific mathematic(al) representation
The production capacity of business men；
Step 2：Data analysis is carried out to production order, according to different demands, extraction critical data represents a production and ordered
It is single overall, the mathematical modeling of selection Optimal Production business is established based on critical data；
Step 3：The mathematical modeling of the selection Optimal Production business is solved using immune algorithm.
Preferably, in the step 1, the mathematical expression mode of production capacity includes：Designed productive capacity, vouch production energy
Power, planned productive capacity.
It is highly preferred that the planned productive capacity is according to existing organization of production condition and technology water in the enterprise plan phase
The production capacity that flat factor can be realized；The planned productive capacity of manufacturer is decided by quantity and quality, the employee of equipment
Quantity and technical merit.
Further, the mathematical expression mode of the planned productive capacity of the manufacturer includes：Manufacturer sets existing
Under conditions of standby and employee, can produce within one day product quality can reach the quantity, average every of certain class product of some grade
Produce the cost of a product.
Preferably, in the step 2, in the production order for manufacturing industry, critical data includes：It is raw as defined in client
The credit rating and production cost that term limit, the product number of production, needs reach.
Preferably, the step 3 specifically includes herein below：
Step 3-1：If optimal solution is m manufacturer, m is positive integer；All alternative manufacturers are numbered, then asked
The optimal solution obtained is the numbering of m manufacturer；In immune algorithm, the numbering of m manufacturer regards an antibody, Duo Gekang as
Body constitutes antibody population；When being not previously set m value, m value is found so that model obtains optimal solution；
Step 3-2：The initial value for setting m is m1, then selects m1 manufacturer to carry out order point in all manufacturers
Match somebody with somebody, the numbering of m1 manufacturer is an antibody, and antibody represents a feasible solution；
Step 3-3：An optimal antibody is found in immune algorithm requirement in antibody population, it is therefore desirable to produces initial antibodies
Group；Randomly generate S1 antibody and therefrom choose S2 antibody composition data base, S2 antibody in S1 antibody and data base
The antibody population that an antibody number is S has been collectively constituted, wherein, S=S1+S2, S1, S2 are the integer randomly selected；Antibody population is
For the S group feasible solutions of model, each group be m manufacturer numbering；
Step 3-4：Each antibody in antibody population described in step 3-3 is evaluated；In immune algorithm, pass through ratio
Affinity and antibody concentration compared with antibody and antigen are evaluated individual, and herein, the value of affinity is crucial number described in step 2
According to value, the similarity that antibody concentration is antibody with other antibody in antibody population；Comprehensive affinity and concentration evaluation antibody are excellent
Elegant degree, reproductive probability P is drawn, form parent colony；By initial population by it is expected that breeding potential P carries out descending arrangement, and before taking
S1 individual forms parent colony, while takes in preceding S2 individual deposit data base；
Step 3-5：Whether the optimum individual in the data base for the parent colony that judgment step 3-4 is obtained meets that model is optimal
Solution, terminate and solve if meeting, otherwise the antibody in parent group is selected, intersected, mutation operation obtains new colony, then from
The individual of memory is taken out in data base, collectively forms colony of new generation, then goes to step 3-4, iteration performs；If still do not reach
To satisfied optimal solution, then 3-2 is gone to step, reset m initial value, the optimal solution until searching out model.
Method provided by the invention overcomes the deficiencies in the prior art, can be in the multiple different vendor's productions of customer selecting
When, reasonable distribution is carried out to production order, reached under conditions of meeting that the quality of production is qualified so that production cost is minimum, institute
With the effect that the time is minimum；And the inventive method result of calculation is accurate, calculating speed is fast, is favorably improved production efficiency.
Brief description of the drawings
Fig. 1 is the production Order splitting cloud process flow figure based on immune algorithm that the present embodiment provides.
Embodiment
With reference to specific embodiment, the present invention is expanded on further.
The present invention relates to a kind of production Order splitting cloud processing method based on immune algorithm, includes the life of analysis manufacturer
Production capacity power, produce order data analysis and establish selection manufacturer mathematical modeling, with immune algorithm to select Optimal Production
The mathematical modeling of business solves.Industry is manufactured with reference to specific, the production capacity of manufacturer is analyzed, production capacity is quantified,
With the production capacity of specific data representation manufacturer；Data analysis is carried out to production order, the data variable of key is extracted, builds
The mathematical modeling of vertical selection Optimal Production business；The mathematical modeling of the selection Optimal Production business of foundation is asked using immune algorithm
Solution, trying to achieve should be by Order splitting to which specific manufacturer.Comprise the following steps that：
Step 1：Industry is manufactured with reference to specific, the production capacity of manufacturer is analyzed, production capacity is quantified, apparatus
The mathematic(al) representation of body represents the production capacity of manufacturer.
Production capacity is to reflect a technical parameter of the working ability that enterprise is possessed, and it can also reflect the life of enterprise
Production scale, it is an important indicator for reflecting enterprise's production possibility.Production capacity has a variety of different expression ways, including：
Designed productive capacity, check-up on production capacity and planned productive capacity etc..Wherein, planned productive capacity is also referred to as actual ability, is
The production capacity that can be realized according to factors such as existing organization of production condition and technical merits in the enterprise plan phase.Producing
In manufacturing enterprise, the planned productive capacity of manufacturer is decided by quantity and quality, the quantity of employee and technical merit of equipment.
In order allocation method is produced, best embodied using the planned productive capacity of manufacturer in manufacturer's prescribed time-limit
Production scale.Assuming that manufacturer, under conditions of existing equipment and employee, can produce product quality within one day can reach B
The A products of grade are N number of, and the average cost for often producing a product is L members；A products are the specific product classification of manufacturer's production
Such as lunch box, carton or hot-water bottle, B grades are quality that the product that manufacturer is produced with existing technical merit can reach etc.
Level；N number of i.e. manufacturer can produce N number of A products for one day, and N is integer.L members produce a product, the material resources spent and people
The totle drilling cost of power.
Step 2：Data analysis is carried out to production order, and establishes the mathematical modeling of selection Optimal Production business.
The data for producing order are analyzed, it is overall that extraction critical data represents a production order.Manufacturing
In the production order of industry, critical data have product term as defined in client, production product number and need quality for reaching etc.
Level and production cost.Different clients is different to the demand for producing order, and client may expect to produce institute within the most short time
The product needed, it is also possible to it is expected to produce spent total production cost minimum, it is also possible to time limit as defined in expectation and cost
Total production cost when being optimal, account for certain proportional roles.
According to the different demands of client, the mathematical modeling for selecting Optimal Production business is established.The present embodiment is to cause client's
Exemplified by total production cost is optimal, the mathematical modeling of selection Optimal Production business is established.Assuming that customer requirement produced A products n in d days
Individual and product quality grade reaches B grades.M manufacturer of selection carries out Order splitting, the product number difference produced daily
For N1, N2 ..., Nm, often produce the cost that is spent of a product be respectively L1, L2 ..., Lm (unit is member), therefore flower
The totle drilling cost y of expense is：
Y=(N1*L1+N2*L2+ ...+Nm*Lm) * d,
And (N1+N2+ ...+Nm) * d >=n.
Step 3：The mathematical modeling for selecting Optimal Production business is solved using immune algorithm.
Using immune algorithm to the mathematical modeling in step 2
Y=(N1*L1+L2*P2+...+Nm*Lm) * d
(N1+N2+…+Nm)*d≥n
Seek optimal solution.It is specific as follows：
Step 3-1：If optimal solution is m manufacturer so that the totle drilling cost for producing cost is minimum.To all alternative production
Business is numbered, then the optimal solution tried to achieve is the numbering of m manufacturer.In immune algorithm, the numbering of m manufacturer is regarded as
One antibody, multiple antibody constitute antibody population.Client can set m value, if client is not provided with m value, it should find m
Value cause model to obtain optimal solution.
Step 3-2：This sentences client and is not provided with exemplified by m value, the optimal solution of solving model.M initial value is set, such as
The initial value for setting m is 5, then selects 5 manufacturers to carry out Order splitting.The numbering of 5 manufacturers is an antibody, is such as considered
The problem of comprising 31 alternative manufacturers, therefrom choose 5 manufacturers as Order splitting.Antibody [2,7,15,21,29] generation
One feasible solution of table.It represents that the manufacturer of numbering 2,7,15,21,29 is chosen as the manufacturer of Order splitting.
Step 3-3：An optimal antibody is found in immune algorithm requirement in antibody population, it is therefore desirable to produces initial antibodies
Group.Randomly generate S1 antibody and therefrom choose S2 antibody composition data base, S2 antibody in S1 antibody and data base
The antibody population that an antibody number is S, wherein S=S1+S2 are collectively constituted.Antibody population is the S group feasible solutions of model, each group
It is the numbering of m manufacturer.
Step 3-4：Each antibody in step 3-3 antibody populations is evaluated.By comparing antibody in immune algorithm
Individual is evaluated with the affinity (i.e. fitness value calculation) and antibody concentration of antigen, the value of affinity is y value herein,
Antibody concentration is the similarity of antibody and other antibody in antibody population.Comprehensive affinity and the concentration evaluation outstanding degree of antibody,
Draw reproductive probability p-shaped into parent colony.Initial population is subjected to descending arrangement by expectation breeding potential P, and takes preceding S1 individual
Parent colony (antibody population) is formed, while is taken in preceding S2 individual deposit data base.
Step 3-5：Whether the optimum individual in the data base for the parent colony that judgment step 3-4 is obtained meets that model is optimal
Solution, solved if satisfied, then terminating；Otherwise the antibody in parent group is selected, intersected, mutation operation obtains new colony, then
The individual of memory is taken out from data base, collectively forms colony of new generation, then turns to perform step 3-4, iteration performs J times.
If being still not reaching to satisfied optimal solution, turn to perform step 3-2, m initial value is reset, until searching out model
Optimal solution.
Experiment shows that method solving result provided by the invention is accurate, and computational efficiency is high.
It is described above, only presently preferred embodiments of the present invention, it is not any to the present invention in form and substantial limitation,
It should be pointed out that for those skilled in the art, on the premise of the inventive method is not departed from, can also make
Some improvement and supplement, these are improved and supplement also should be regarded as protection scope of the present invention.All those skilled in the art,
Without departing from the spirit and scope of the present invention, when made using disclosed above technology contents it is a little more
Dynamic, modification and the equivalent variations developed, it is the equivalent embodiment of the present invention；Meanwhile all substantial technologicals pair according to the present invention
The variation, modification and evolution for any equivalent variations that above-described embodiment is made, still fall within the scope of technical scheme
It is interior.
Claims (6)
- A kind of 1. production Order splitting cloud processing method based on immune algorithm, it is characterised in that：This method is by following 3 steps Composition：Step 1：The production capacity of manufacturer is analyzed, by production capacity quantification, manufacturer is represented with specific mathematic(al) representation Production capacity；Step 2：Data analysis is carried out to production order, according to different demands, it is whole that extraction critical data represents a production order Body, the mathematical modeling of selection Optimal Production business is established based on critical data；Step 3：The mathematical modeling of the selection Optimal Production business is solved using immune algorithm.
- A kind of 2. production Order splitting cloud processing method based on immune algorithm as claimed in claim 1, it is characterised in that：Institute State in step 1, the mathematical expression mode of production capacity includes：Designed productive capacity, check-up on production capacity, planned productive capacity.
- A kind of 3. production Order splitting cloud processing method based on immune algorithm as claimed in claim 2, it is characterised in that：Institute Stating planned productive capacity can be realized according to existing organization of production condition and technical merit factor in the enterprise plan phase Production capacity；The planned productive capacity of manufacturer is decided by quantity and quality, the quantity of employee and technical merit of equipment.
- A kind of 4. production Order splitting cloud processing method based on immune algorithm as claimed in claim 3, it is characterised in that：Institute Stating the mathematical expression mode of the planned productive capacity of manufacturer includes：Manufacturer is under conditions of existing equipment and employee, and one It can produce quantity, the average cost for often producing a product that product quality can reach certain class product of some grade.
- A kind of 5. production Order splitting cloud processing method based on immune algorithm as claimed in claim 1, it is characterised in that：Institute State in step 2, in the production order for manufacturing industry, critical data includes：Product term, the product of production as defined in client The credit rating and production cost that number, needs reach.
- A kind of 6. production Order splitting cloud processing method based on immune algorithm as claimed in claim 1, it is characterised in that：Institute State step 3 and specifically include herein below：Step 3-1：If optimal solution is m manufacturer, m is positive integer；All alternative manufacturers are numbered, then tried to achieve Optimal solution is the numbering of m manufacturer；In immune algorithm, the numbering of m manufacturer regards an antibody, multiple antibodyomes as Into antibody population；When being not previously set m value, m value is found so that model obtains optimal solution；Step 3-2：The initial value for setting m is m1, then selects m1 manufacturer to carry out Order splitting, m1 in all manufacturers The numbering of individual manufacturer is an antibody, and antibody represents a feasible solution；Step 3-3：An optimal antibody is found in immune algorithm requirement in antibody population, it is therefore desirable to produces initial antibodies group；With Machine produces S1 antibody and therefrom chooses S2 antibody composition data base, common group of S2 antibody in S1 antibody and data base The antibody population for being S into an antibody number, wherein, S=S1+S2, S1, S2 are the integer randomly selected；Antibody population is model S group feasible solutions, each group be m manufacturer numbering；Step 3-4：Each antibody in antibody population described in step 3-3 is evaluated；In immune algorithm, by relatively more anti- The affinity and antibody concentration of body and antigen are evaluated individual, and herein, the value of affinity is critical data described in step 2 Value, antibody concentration are the similarity of antibody and other antibody in antibody population；Comprehensive affinity and the concentration evaluation outstanding journey of antibody Degree, reproductive probability P is drawn, form parent colony；Initial population is subjected to descending arrangement by expectation breeding potential P, and takes first S1 Individual forms parent colony, while takes in preceding S2 individual deposit data base；Step 3-5：Whether the optimum individual in the data base for the parent colony that judgment step 3-4 is obtained meets model optimal solution, Terminate and solve if meeting, otherwise the antibody in parent group is selected, intersected, mutation operation obtains new colony, then from note Recall the individual that memory is taken out in storehouse, collectively form colony of new generation, then go to step 3-4, iteration performs；If still it is not reaching to Satisfied optimal solution, then go to step 3-2, resets m initial value, the optimal solution until searching out model.
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