CN110516958A - A kind of resource regulating method in face of manufacturing process - Google Patents
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Abstract
The invention discloses a kind of resource regulating methods in face of manufacturing process to initialize the parameter in resource regulating method including establishing the resource scheduling scheme set towards manufacturing process and the mapping relations between resource regulating method solution space；It is improved based on speed more new formula of the automatic adjusument mechanism in organism to combination solution, population is combined in the scheduling for manufacturing process arrived required by optimization；Each combination solution generates a random number, and then operates to scheduling combination population progress single point crossover operation and basic bit mutation, generates new scheduling combination population；Using the fitness for respectively combining solution in Combination nova population is calculated based on the fitness function for minimizing total complete time, finds more preferably scheduling of resource and distribute；When meeting the preset termination condition of this method, the resource scheduling scheme of optimal Manufacturing Process is exported.The present invention has very strong global optimizing ability, obtains feasible optimization scheduling of resource assembled scheme in a short time.
Description
Technical field
The present invention relates to a kind of resource regulating methods in face of manufacturing process, belong to manufacturing technology field.
Background technique
Since most of method in enterprise's production for the resource allocation problem of manufacturing process is low in search efficiency, obtain
Allocation result it is of poor quality, spend human and material resources, influence enterprises production efficiency, thus it is existing majority resource coordination method reality
Border application power is not strong, can not improve the operational efficiency of enterprise with improving, can not also effectively reduce the operating cost of enterprise.Cause
This, studying, there is the efficient feasible dispatching method of strong robustness to solve the resource allocation problem in production system, be come with this
It realizes reasonable resource allocation, improves enterprise operation benefit, there is important theoretical value and practical significance.
Summary of the invention
It is an object of the invention to overcome deficiency in the prior art, a kind of scheduling of resource side in face of manufacturing process is provided
The disadvantages of method can effectively overcome the search efficiency of combination of resources solution low, and allocation result is second-rate, for the life of different scales
Production system all has stronger optimizing ability, the resource scheduling scheme of acquisition high quality that can be stable.
In order to achieve the above objectives, the present invention adopts the following technical solutions realization:
A kind of resource regulating method in face of manufacturing process, described method includes following steps:
The mapping relations between resource scheduling scheme set and resource regulating method solution space are established, by each scheduling scheme
The corresponding combination solution of process sequence, the corresponding scheduling of the set of scheduling scheme combines population；
The individual optimum combination and population optimum combination of initialization combination solution；
According to bio-hormone adjustment mechanism, the search speed formula of combination solution is improved, and then by combination of resources
More new formula is updated combination solution, determines speed and the position of each Combination nova solution；
Each combination is solved and generates a random number, single point crossover operation and basic bit mutation are carried out to scheduling combination population
Operation, more Combination nova solution, obtains new scheduling combination population again；
The fitness of Combination nova solution, and then more Combination nova are calculated using the fitness function based on minimum total complete time
The individual optimum combination and population optimum combination of solution；
When meeting preset stopping criterion for iteration, output population optimum combination resource tune corresponding with population optimum combination
Degree scheme.
Preferably, the scale of the population takes 20~100, and the number of iterations of the resource regulating method takes 100~
4000 times.
Preferably, the individual optimum combination of initialization combination solution and the method for population optimum combination include the following steps:
Solution is all combined by the random number that the random number in (0,1) forms using the generation of rand function；
Random number combination solution is converted into the process step combinations solution based on process using sort function and ceil function；
The fitness of each combination solution in initial schedule combination population is calculated according to fitness function, thus to combination solution
Individual optimum combination and population optimum combination are initialized.
Preferably, the method for determining combination solution speed includes the following steps:
Inertial factor w (k) is determined based on automatic adjusument mechanism in organism:
Wherein, k represents current iteration number, and w (k) represents the inertial factor of kth time iteration, wmaxRepresent inertial factor
Maximum value, wminThe minimum value of inertial factor is represented, T represents production task completion date threshold value, and n represents Hill coefficient, w0It represents
The initial value of inertial factor；
The speed of combination solution is calculated using improved speed more new formula shown in formula (2):
Vid(k+1) speed of d-th of variable of i-th of combination solution after k+1 iteration is represented；Vid(k) it represents and passes through
The speed of d-th of variable of i-th of combination solution after k iteration；c1、c2Represent Studying factors or accelerated factor；r1、r2Represent [0,
1] equally distributed random number in；Pid(k) represent after k iteration in population i-th of combination solve itself search it is optimal
D-th of variable of position；Xid(k) position of d-th of variable of i-th of combination solution after k iteration is represented；Pgd(k) it represents
D-th of variable of the optimal location that population searches after k iteration.
Preferably, the position of each combination solution is determined using formula (3):
Xid(k+1)=Xid(k)+Vid(k+1) (3)
Wherein: Xid(k+1) position of d-th of variable of i-th of combination solution after k+1 iteration is represented.
Preferably, the individual optimum combination of more Combination nova solution and the method for population optimum combination include the following steps:
The fitness of process step combinations solution is compared with the fitness of individual optimum combination, if the former is less than the latter,
The current location of process step combinations solution is then assigned to its individual optimum combination, otherwise retains individual optimum combination；
Population is combined for scheduling, the fitness of the fitness of each process step combinations solution and population optimum combination is compared
Compared with, if the former be less than the latter, then the current location of the process step combinations solution is assigned to population optimum combination, otherwise retain population most
Excellent combination.
Preferably, shown in the fitness function such as formula (4):
T=max (CWij) 1≤i≤n,1≤j≤Gi
Wherein, T represents the total complete time of manufacturing operation, CWijRepresent the deadline of the jth procedure of workpiece i, n generation
Table workpiece number, GiRepresent the operation quantity of workpiece i.
Preferably, the crossover probability of the single point crossover operation takes 0.6~0.99.
Preferably, the mutation probability of the basic bit mutation operation takes 0.005~0.01.
Compared with prior art, the beneficial effects obtained by the present invention are as follows being: according to bio-hormone adjustment mechanism, to dispatching party
The search speed formula that combination solution is found in method is improved, and then is carried out more by combination of resources more new formula to combination solution
Newly, speed and the position for determining each Combination nova solution are used according to speed and position based on the adaptation for minimizing total complete time
It spends function and calculates fitness, the individual optimum combination and population optimum combination of more Combination nova solution can effectively increase population
Diversity expands the solution space range of this method, overcomes and is easy the shortcomings that falling into local convergence too early, for the life of different scales
Production system all has stronger optimizing ability, the resource scheduling scheme of acquisition high quality that can be stable, to improve production system
Runnability.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is a kind of flow chart of the resource regulating method in face of manufacturing process provided according to embodiments of the present invention；
Fig. 2 is the method flow diagram that Fig. 1 is further refined；
Fig. 3 is to combine the process schematic that solution is converted into process step combinations solution according to random number provided in an embodiment of the present invention；
Fig. 4 is according to basic bit mutation schematic diagram provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
It as shown in Figure 1 and Figure 2, is a kind of stream of resource regulating method in face of manufacturing process provided in an embodiment of the present invention
Cheng Tu includes the following steps:
Step 1: the initialization of parameter and population:
Pre-set population scale, the number of iterations, coding and decoding mode, fitness function, combination solution more new formula ginseng
Number, crossover probability and mutation probability.The selection of population scale and the number of iterations affects resource regulating method and searches for most
The precision of excellent solution.Population scale or the number of iterations selection are excessive, although it is satisfactory that dispatching method can be made to be more likely to get
Optimal solution, but calculation amount is increased, expend the time；Population scale or the number of iterations selection are too small, can save operation time, but meeting
The precision for the optimal solution for obtaining dispatching method reduces.Therefore it need to comprehensively consider, look in optimal solution precision and between operation time
To an equalization point, general population scale takes 20~100, and the number of iterations takes 100~4000 times.Solve resource in manufacturing process
The key of assignment problem be establish scheduling scheme with combine the mapping relations between solution.The corresponding combination of each scheduling scheme
Solution, the set of scheduling scheme correspond to entire scheduling combination population.Coding is that scheduling scheme is transformed into combination solution, it is assumed that one
A task has x workpiece to be processed, and each workpiece has y procedure, shares x*y procedure, then each combination solution is by random alignment
Y 1,2 ..., x composition, i.e., each combination solution has x*y dimension, and here it is cataloged procedures.Decoding is will to combine solution to be converted to
For corresponding scheduling scheme, continue above-mentioned it is assumed that when each combination solution is by y 1,2 of random alignment ..., when x is formed, the 1st
1 occurred represents the 1st procedure of workpiece 1, and the 1 of the 2nd appearance represents the 2nd procedure of workpiece 1, until the 1 of y-th of appearance
Represent the y procedure of workpiece 1, and so on, combination solution is transformed into corresponding process sequence, here it is decoding process.
For the resource allocation problem in manufacturing process, the fitness function based on the resource regulating method for minimizing total complete time is such as
Shown in lower:
T=max (CWij) 1≤i≤n,1≤j≤Gi
Wherein, T represents the total complete time of manufacturing operation, CWijRepresent the deadline of the jth procedure of workpiece i, n generation
Table workpiece number, GiRepresent the operation quantity of workpiece i.
Initialization scheduling combination population: it is based on MATLAB emulation platform, is generated all using rand function by (0,1)
Random number composition random number combine solution.There are 4 workpiece to be processed with a task, for each workpiece there are 4 procedures, then
Program statement is A=rand (1,16).Rand function generates the array being made of random number equally distributed between (0,1),
I.e. the sentence generates the array that 1 row 16 arranges, and here it is the generating process of a combination solution.
Initialize individual optimum combination pbest and population optimum combination gbest: first with sort function and ceil function
Random number combination solution is transformed into the process step combinations solution based on process, initial schedule combination is then calculated according to fitness function
The fitness of each combination solution in population, to be carried out to individual optimum combination pbest and population optimum combination gbest initial
Change.The example for continuing previous step combines solution to random number by sort function and carries out ascending order arrangement, and specific procedure sentence is
[I, J]=sort (A).Sort function is to carry out ascending order arrangement to array A, and I is ranking results, i.e. sequence array；J is after sorting
Subscript array.Then the process step combinations solution based on process is obtained by ceil function, specific procedure sentence is C=ceil (J/
4).First by subscript array divided by workpiece number, ceil function is then used, the effect of ceil function is taken towards positive infinity direction
It is whole, thus obtain the process step combinations solution based on process.If the process number of each workpiece is different, to extra process into
Row is deleted, and the process step combinations solution for meeting manufacturing operation requirement is made.Each process group is finally calculated according to fitness function
The fitness for closing solution, to be initialized to individual optimum combination pbest and population optimum combination gbest.
Step 2: combination solution is updated according to location update formula and improved speed more new formula:
It is updated according to speed of the improved speed more new formula to random number combination solution.Original speed more new formula
It is as follows:
Vid(k+1)=w*Vid(k)+c1*r1*(Pid(k)-Xid(k))+c2*r2*(Pgd(k)-Xid(k))
Wherein, k represents current iteration number, Vid(k+1) d-th of change of i-th of combination solution after k+1 iteration is represented
The speed of amount, w represent inertial factor, Vid(k) speed of d-th of variable of i-th of combination solution after k iteration, c are represented1、
c2Represent Studying factors or accelerated factor, r1、r2Represent equally distributed random number in [0,1], Pid(k) it represents and passes through k iteration
D-th of variable of the optimal location that i-th of combination solution searches itself in population afterwards, Xid(k) it represents i-th after k iteration
The position of d-th of variable of a combination solution, Pgd(k) d-th of change of the optimal location that population searches after k iteration is represented
Amount.Automatic adjusument mechanism meets Hill function in organism, and Hill function is by increasing function (Fup) and decreasing function (Fdown)
Composition, as follows:
Wherein, C is hormone concentration variable, i.e. independent variable；N is Hill coefficient, n >=1；T is hormone concentration threshold value, T > 0；n
With T determining function slope of a curve.It follows that the inertial factor w design formula based on automatic adjusument mechanism in organism
It is as follows:
Wherein, k represents current iteration number, and w (k) represents the inertial factor of kth time iteration, wmaxRepresent inertial factor
Maximum value, wminThe minimum value of inertial factor is represented, T is represented threshold value (T > 0), and n represents Hill coefficient (n >=1), w0Represent inertia
The initial value of the factor.Studying factors c1、c2With random number r1、r2Using correlation coefficient process, by the c in raw velocity more new formula1*
r1Become (1-r2)*c1*r1, c2*r2Become (1-r2)*c2*(1-r1).In conclusion the following institute of improved speed more new formula
Show:
It is updated according to position of the location update formula to random number combination solution.Location update formula is as follows:
Xid(k+1)=Xid(k)+Vid(k+1)
Step 3: the single point crossing based on crossover probability Pc is carried out to scheduling combination population:
Based on MATLAB emulation platform, solution i is combined to random number using rand function and generates a random number C (i).
I is solved for combination, judges whether corresponding random number C (i) is less than crossover probability Pc, is to lock combination solution i, it is no
Then retain combination solution i.The frequency of use of crossover probability Pc decision crossover operation.Crossover probability is bigger, and it is excellent that combination solution loses its
A possibility that property, is bigger, and crossover probability is smaller, and search process may fall into stagnation.Therefore comprehensively consider, crossover probability
Pc generally takes 0.6~0.99.
First complete random number scheduling combination population to be scanned, carries out single point crossing two-by-two for the combination solution of locking, i.e.,
Crosspoint is set as the half of combination solution length, two parent combination solutions are then subjected to front point or rear portion on this crosspoint
The exchange divided generates two new filial generations and combines solution, gradually carry out in this way, to complete to entire random number scheduling group
Close crossover operation of the population based on crossover probability Pc.
Step 4: the basic bit mutation based on mutation probability Pm is carried out to scheduling combination population:
Based on MATLAB emulation platform, a random number M is generated using the dimension j that rand function combines solution i to random number
(ij)。
For the dimension j of combination solution i, judge whether corresponding random number M (ij) is less than mutation probability Pm, is to utilize
Rand function initializes the value on the dimension j of combination solution i, otherwise retains the value on the dimension j of combination solution i.Basic bit
Variation, which refers to, carries out mutation operator in combination solution with the value in mutation probability Pm randomly selected one of them or several dimensions.
The changed probability of value after the completion of mutation probability Pm refers to crossover operation, in each dimension of each combination solution.Variation is general
Rate is bigger, and the randomness of search process is bigger, and mutation probability is smaller, and search process is easily trapped into local optimum.Therefore synthesis is examined
Consider, mutation probability Pm generally takes 0.005~0.01.
Step 5: individual optimum combination pbest and population optimum combination gbest are updated:
Wherein, detailed process includes:
Based on MATLAB emulation platform, new random number scheduling combination population is converted using sort function and ceil function
Combination population is dispatched as process.
The fitness of each process step combinations solution is found out according to fitness function.
For process step combinations solution i, its fitness is compared with the fitness of individual optimum combination pbest, if
The former is less than the latter, then the current location of process step combinations solution i is assigned to its individual optimum combination pbest, otherwise retains individual
Optimum combination pbest.
Combination population is dispatched for entire process, by the fitness of each process step combinations solution and population optimum combination gbest
Fitness be compared, if the former be less than the latter, then the current location of the process step combinations solution is assigned to population optimum combination
Otherwise gbest retains population optimum combination gbest.
Step 6: judge whether to meet preset stopping criterion for iteration, is to terminate iterative process, otherwise goes to step 2,
Carry out the iterative search of a new round；
Step 7: output population optimum combination gbest and corresponding resource scheduling scheme.
It should be noted that, in this document, the terms "include", "comprise" or any other variant be intended to it is non-exclusive
Property include so that include a series of elements process, method, article or device not only include those elements, but also
Further include other elements that are not explicitly listed, or further include for this process, method, article or device it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that wanted including this
There is also other identical elements in the process, method of element, article or device.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest range of cause.
Claims (7)
1. a kind of resource regulating method in face of manufacturing process, which is characterized in that described method includes following steps:
The mapping relations between resource scheduling scheme set and resource regulating method solution space are established, by the work of each scheduling scheme
The combination solution of the corresponding manufacturing recourses distribution of sequence sequence, the corresponding scheduling combination population of the set of scheduling scheme；
The individual optimum combination and population optimum combination of initialization combination solution；
According to bio-hormone adjustment mechanism, the search speed formula of combination solution is improved, and then public affairs are updated by combination of resources
Formula is updated combination solution, determines speed and the position of each Combination nova solution；
Each combination is solved and generates a random number, single point crossover operation is carried out to scheduling combination population and basic bit mutation is grasped
Make, again more Combination nova solution, obtains new scheduling combination population；
Using the fitness for calculating Combination nova solution based on the fitness function for minimizing total complete time, and then more Combination nova solution
Individual optimum combination and population optimum combination；
When meeting preset stopping criterion for iteration, output population optimum combination scheduling of resource side corresponding with population optimum combination
Case.
2. the resource regulating method according to claim 1 in face of manufacturing process, which is characterized in that initialization combination solution
The method of individual optimum combination and population optimum combination includes the following steps:
Solution is all combined by the random number that the random number in (0,1) forms using the generation of rand function；
Random number combination solution is converted into the process step combinations solution based on process using sort function and ceil function；
The fitness that each combination solution in initial schedule combination population is calculated according to fitness function, thus to the individual of combination solution
Optimum combination and population optimum combination are initialized.
3. the resource regulating method according to claim 1 in face of manufacturing process, which is characterized in that determine combination solution speed
Method include the following steps:
Inertial factor w (k) is determined based on automatic adjusument mechanism in organism:
Wherein, k represents current iteration number, and w (k) represents the inertial factor of kth time iteration, wmaxRepresent the maximum of inertial factor
Value, wminThe minimum value of inertial factor is represented, T represents production task completion date threshold value, and n represents Hill coefficient, w0Represent inertia
The initial value of the factor；
The speed of combination solution is calculated using improved search speed more new formula shown in formula (2):
Vid(k+1) speed of d-th of variable of i-th of combination solution after k+1 iteration is represented；Vid(k) it represents by k times repeatedly
The speed of d-th of variable of i-th of combination solution after generation；c1、c2Represent Studying factors or accelerated factor；r1、r2It represents in [0,1]
Equally distributed random number；Pid(k) optimal location that i-th of combination solution searches itself in population after k iteration is represented
D-th of variable；Xid(k) position of d-th of variable of i-th of combination solution after k iteration is represented；Pgd(k) it represents and passes through
D-th of variable of the optimal location that population searches after k iteration.
4. the resource regulating method according to claim 1 in face of manufacturing process, which is characterized in that of more Combination nova solution
The method of body optimum combination and population optimum combination includes the following steps:
The fitness of process step combinations solution is compared with the fitness of individual optimum combination, if the former is less than the latter, then will
The current location of process step combinations solution is assigned to its individual optimum combination, otherwise retains individual optimum combination；
Population is combined for scheduling, the fitness of each process step combinations solution is compared with the fitness of population optimum combination,
If the former is less than the latter, then the current location of the process step combinations solution is assigned to population optimum combination, it is optimal otherwise to retain population
Combination.
5. the resource regulating method according to any one of claims 1 to 4 in face of manufacturing process, which is characterized in that described
Shown in fitness function such as formula (3):
T=max (CWij) 1≤i≤n,1≤j≤Gi (3)
Wherein, T represents the total complete time of manufacturing operation, CWijThe deadline of the jth procedure of workpiece i is represented, n represents work
Number of packages, GiRepresent the operation quantity of workpiece i.
6. the resource regulating method according to claim 1 in face of manufacturing process, which is characterized in that the single point crossing behaviour
The crossover probability of work takes 0.6~0.99.
7. the resource regulating method according to claim 1 in face of manufacturing process, which is characterized in that the basic bit mutation
The mutation probability of operation takes 0.005~0.01.
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