CN105956689B - A kind of transport and procreative collaboration dispatching method based on Modified particle swarm optimization - Google Patents
A kind of transport and procreative collaboration dispatching method based on Modified particle swarm optimization Download PDFInfo
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Abstract
The invention discloses a kind of transport based on Modified particle swarm optimization and procreative collaboration dispatching method, it is characterized in that carrying out as follows：Workpiece is carried out batching by 1；2 set algorithm parameters；3 produce initial population；4 calculate fitness value；5 judge whether end condition meets, and export globally optimal solution if meeting, otherwise continue；6 update locally optimal solution and globally optimal solution；7 update particle position and speed；8 pairs of each particles carry out crossover operation；9 pairs of each particles carry out immigrant's operation；10 adjustment particle positions, return to step 4.The present invention can realize the optimization of enterprise's overall economic benefit, so as to reduce entreprise cost, the service level of enterprise.
Description
Technical field
The invention belongs to supply chain field, specifically a kind of transport and procreative collaboration based on Modified particle swarm optimization
Dispatching method, in the Production scheduling management of enterprise.
Background technology
Under Vehicles Collected from Market environment, the core competitiveness of manufacturing enterprise is no longer simple manufacturing capacity, but supply
Chain collaboration capabilities.The aggravation of market competition, makes the cooperation between enterprise more and more closer.In order to which technology, resource etc. are concentrated on
Some processes are often contracted out to multiple manufacturers by the key link of production, enterprise.Different manufacturers is often positioned in different
Geographical position, which results in there are different haulage times between enterprise and different manufacturers.Enterprise only manufactures with other
Enterprise cooperates with together carries out global control by links such as transport, productions, further carries out production system and logistics transportation system
Combined optimization, the timely supply of consumer product could be met to greatest extent, so as to obtain the maximization of overall economic benefit, lifted
The competitiveness of enterprise.
Cooperative scheduling is optimization method of the class towards supply chain, by the way of accurately dispatching, each in design supply chain
The cooperative scheduling scheme of link, realizes the optimization of enterprise's overall economic benefit, so that the service level of enterprise.
At present, the research to cooperative scheduling problem is concentrated mainly under traditional mode of production pattern, in this kind of production model, and one
Platform equipment can handle an operation or the operation of a collection of fixed qty simultaneously, but often not account for multiple manufacturers point
Situation of the cloth in diverse geographic location.In real industry, this kind of production model is present.And traditional dispatching method is often
By haulage time ignore or it is abstract be identical, therefore do not adapt to production requirement instantly.
The content of the invention
The present invention is in order to which the weak point for overcoming prior art to exist is matched somebody with somebody there is provided a kind of production based on particle group optimizing
Coordinated dispatching method is sent, to which the optimization of overall economic benefit can be realized, so as to reduce production cost, lifting work effect
Rate.
The present invention adopts the following technical scheme that to solve technical problem：
A kind of the characteristics of transport and procreative collaboration dispatching method based on Modified particle swarm optimization of the present invention is will to be in work
N workpiece at part set is carried out after batch processing, is delivered at m platform equipment and is produced and processed by haulage vehicle；The n
The workpiece set that individual workpiece is constituted is designated as J={ J1,J2,…,Jj,…,Jn, JjRepresent j-th of workpiece, 1≤j≤n；By j-th
Workpiece JjSize be designated as sj, process time be designated as pj；The m platforms equipment is designated as M={ M1,M2,…,Mi,…,Mm, MiRepresent the
I platform equipment, 1≤i≤m；By i-th equipment MiThe speed of processing workpiece is designated as vi, i-th will be reached at the workpiece set
Equipment MiHaulage time be designated as ti；The volume of the volume of haulage vehicle and processing workpiece device is designated as C；
Described transport with procreative collaboration dispatching method is to carry out as follows：
Step 1, the workpiece in the workpiece set J is ranked up by the order of process time non-increasing, if process time
It is identical, it is ranked up by the order of the non-increasing of workpiece size, so that the workpiece set J ' after being sorted；
Step 2, it the 1st unappropriated workpiece in the workpiece set J ' after the sequence is put into can accommodate the described 1st
The size and remaining space of individual unallocated workpiece it is minimum batch in, batch remaining space be all in corresponding to being put into crowd of volume C
The difference of workpiece size sum；If can not accommodate the size of the 1st unallocated workpiece in the remaining space criticized, generation volume is
New batch of C, and the 1st unappropriated workpiece is added in new batch, until all workpiece in the workpiece set J ' are all distributed
To in corresponding batch；
Step 3, all batches obtained in step 2 are arranged by batch processing time non-increasing, obtain batch processing set B=
{b1,b2,…,bq,…,bl, bqQ-th batch is represented, the process time of q-th batch is designated as p(q), q-th crowd of bqProcessing when
Between p(q)It is by q-th crowd of bqMost long workpiece of middle process time is determined；Lot count is designated as l, Table
Show the smallest positive integral not less than x；
Step 4, the parameters for initializing particle cluster algorithm, including：Total number of particles N, iterations L, greatest iteration time
Number Lmax, crossover probability Pc1And Pc2, 1≤L≤Lmax；And initialize L=1；
Step 5, generation initial population, obtain the initial position of k-th of particle in L generationsAnd initial velocity WithDifference table
Show position and speed of k-th of particle on d dimensions search space in L generations, wherein, 1≤d≤l；1≤k≤N；
Step 6, the fitness for calculating k-th of particle in L generationsSo as to obtain the part of k-th of particle in L generations
Optimal solutionWherein,Represent that k-th of particle ties up search space in d in L generations
On optimal location；
Step 7, repeat step 6, obtain the locally optimal solution of N number of particle in L generations, and therefrom select maximum adaptation angle value
Corresponding optimal solution is designated as the globally optimal solution in L generationsWhereinRepresent
Whole particle colony ties up the optimal location on search space in d in L generations；
Step 8, the position according to k-th of particle in L generationsAnd speedK-th of L+1 generations is calculated respectively
The position of particleAnd speedSo as to obtain L+1 for the position of N number of particle and speed in population；
Step 9, the adjustment L+1 for N number of particle in population position so that the N in the L+1 generations after being adjusted
The position of individual particle；
Step 10, calculating L+1 are for k-th of particle in populationFitnessAnd with L k-th in
The fitness of particleIt is compared, regard the corresponding particle position of larger fitness value as k-th particle in L+1 generations
Optimal solution
Step 11, repeat step 10, thus obtain L+1 generation in N particles optimal solution and therefrom select maximum adaptation degree
It is worth globally optimal solution of the corresponding optimal solution as L+1 generations
Step 12, L+1 is assigned to L, judges L ＜ LmaxWhether set up, if so, then perform step 8；Otherwise, represented
Into LmaxSecondary iteration, and obtain globally optimal solutionWith globally optimal solutionCorresponding scheduling scheme is adjusted as optimal
Degree scheme.
The characteristics of production scheduling method of the present invention based on particle group optimizing, lies also in,
During the step 5 creates initial population, N-1 particle is produced by random fashion, remaining one
Particle is to produce as follows：
Step 5.1, orderRepresent i-th equipment MiUpper q-th crowd of bqCompletion date, niRepresent i-th equipment MiOn
The quantity criticized of processing, AiFor i-th equipment MiFree time；Initializationni=1, q=1, Ai=0；
Step 5.2, judge whether q ＞ l set up, if so, then represent one particle of generation；Otherwise, step 5.3 is performed；
Step 5.3, utilize formula (1) obtain q-th crowd of bqCompletion date in i-th equipmentSo as to obtain q
Individual crowd of bqCompletion date in m platform equipment：
In formula (1), max { x, y } represents to take the greater in x and y；
Step 5.4, from q-th crowd of bqSetting corresponding to minimum makespan is selected in completion date in m platform equipment
It is standby, equipment min is designated as, by q-th of batch bqTransport and be processed on equipment min；Again by nmin+ 1 is assigned to nmin,It is assigned to Amin, q+1 is assigned to after q, performs step 5.2.
The step 8 is to carry out as follows：
Step 8.1, the initial position by k-th of particle in L generationsIt is assigned toWherein,For withVariable with identical meanings, comparesWithWhether the value of middle corresponding element is identical,
, will if identicalThe value of middle corresponding element is set to 0, if it is different, then willMiddle corresponding element value is set toIn
The value of corresponding element；So as to obtain renewalUpdateIn the value of each element represent the sequence number of equipment；And it is every
Individual element is corresponded with each batch in batch processing set B；
Step 8.2, by renewalCriticizing corresponding to the element of middle non-zero value is dispatched to setting representated by non-zero value successively
It is standby to be above processed, and calculate the completion date of relevant device；So as to obtain equipment completion date set C '=C ' [1] ...,
C ' [i] ..., C ' [m] } and batch size set n '={ n '1,…,n′i,…,n′m}；C ' [i] represents i-th equipment MiIt is complete
Between man-hour, n 'iRepresent i-th equipment MiThe quantity criticized of upper processing；
Step 8.3, by n 'i+ 1 is assigned to n 'i, C ' [i] is assigned to A 'i, wherein, variables A 'iWith AiWith identical meanings；
Step 8.4, by renewalMiddle all values are the batch corresponding to 0 element, by batch processing time non-increasing
Arrangement obtains crowd set B '={ b '1,…,b′q′,…,b′l′, wherein, what l ' expressions updatedIntermediate value is total for 0 element
Number, i.e., unappropriated batch total；
Step 8.5, make q '=1；
If step 8.6, q ' ＞ l ', represent to complete to renewalUpdated again, and perform step 8.9；It is no
Then, step 8.7 is performed；
Step 8.7, formula (2) is utilized to obtain q ' individual batch b 'q′Completion date in i-th equipmentSo as to obtain
Obtain the individual batch b ' of q 'q′Completion date in m platform equipment：
Step 8.8, from the individual batch b ' of q 'q′Selected in completion date in m platform equipment corresponding to minimum makespan
Equipment, equipment min ' is designated as, by the individual batch b ' of q 'q′Equipment min ' is transported above to be processed；Again by n 'min′+ 1 is assigned to
n′min′,It is assigned to A 'min′, q '+1 is assigned to q ', performs step 8.6；
Step 8.9, it is distributed by bernoulli and obtains the l dimension groups R that is made up of 0 and 11, array R1In each element
With again updatingIn each element correspond；If R1In element be 1, then update againMiddle corresponding element
The value of element keeps constant, if R1In element be 0, then update againThe value of middle corresponding element is set to 0；
Step 8.10, by what is updated againIt is used as renewalAnd execution in step 8.2- steps 8.8 is substituted into, from
And obtain after third time renewal
Step 8.11, generalAsWillAsAnd execution in step 8.1- steps 8.10 is substituted into, from
And obtain after third time renewal
Step 8.12, will third time update afterAfter being updated with third timeWith probability Pc1Carry out bernoulli friendship
Fork, the position of k-th of particle in the L generations after being intersected
Step 8.13, generalWithWith probability Pc1Bernoulli intersection is carried out, k-th of particle in L+1 generations is obtained
Speed
Step 8.14, generalWithWith probability Pc1Bernoulli intersection is carried out, k-th of particle in L+1 generations is obtained
Position
Step 8.15, generalWith globally optimal solutionWith probability Pc2Bernoulli intersection is carried out, is obtained after two intersections
Particle, relatively prechiasmal particleWith intersect after two particles fitness, regard the big particle of fitness as
K-th of particle in L+1 generationsPosition；
Step 8.16, calculate L+1 for each particle in population fitness, fitness value is minimumIndividual particle is replaced with the particle randomly generated,Represent the maximum integer no more than x；So as to update L+1
For the position of population.
The step 9 is to be adjusted as follows：
Step 9.1, defined variable f and h, the maximum length for defining Local Search are fmax；Make f=1, i=1, h=m；
Step 9.2, judge f ＜ fmaxWhether set up, if so, then m platforms equipment is arranged by the non-increasing of completion date
Row, perform step 9.3；Otherwise, L+1 is completed for N number of particle position in populationAdjustment；
Step 9.3, i-th equipment M of selectioni；Select h platform equipment Mh；
Step 9.4, judge whether h ＞ 1 set up, if so, then perform step 9.5；Otherwise, L+1 is completed for population
In N number of particle positionAdjustment；
Step 9.5, any α crowd of b of searchingα, bα∈Mi；And any β crowd bβ, bβ∈Mh；If α crowd bα
With β crowd bβFormula (3) is met, then exchanges α crowd bαWith β crowd bβ, and perform step 9.6；Otherwise, step is performed
9.7：
In formula (3), p(α)Represent α crowd bαProcess time, C [i] represent i-th equipment MiCompletion date, p(β)Table
Show β crowd bβProcess time, C [h] represents h platform equipment MhCompletion date,
Step 9.6, batch being arranged by batch process time non-increasing in same equipment will be assigned to, and calculate individual device
Completion date, f is assigned to by f+1, performs step 9.2；
Step 9.7, h-1 is assigned to after h, performs step 9.4.
Compared with the prior art, the present invention has the beneficial effect that：
1st, the present invention is produced in batches under pattern in typical difference, studies the transport and production two benches collaboration of manufacturing enterprise
Scheduling problem, by using modified particle swarm optiziation, is then based on haulage time first against difference workpiece, is criticized in batches
The process velocity of processing time and equipment proposes corresponding scheduling strategy, draws position and the speed of particle；Recycle particle
Current location and speed update rule, update particle position and speed, realize successive ignition, finally obtain optimal solution；Grain
Swarm optimization is the approximate calculation that a kind of performance preferably optimizes manufacture span on available time and the degree of optimization of result
Method；The combined optimization of job batching transport and production in real industry is solved the problems, such as, enterprise's overall economic benefit is realized
Optimization, reduces energy consumption, saves cost, improve the service level of enterprise.
2nd, present invention processing based on haulage time, batch processed time and equipment during initial population is produced
Speed proposes corresponding strategy, and will criticize to be assigned to makes in batch equipment completed at first, thus produces in initial population one by one
Body, other individuals in population are produced in conjunction with random population producing method, the quality of initial population had both been ensure that, and had also ensured that
The diversity of initial population.
3rd, the present invention is to the optimal solution that iteration each time is obtained by being updated, and solves too fast during algorithm search
The problem of ground focuses on locally optimal solution；Location updating rule in add genetic algorithm in crossover operator and one it is adaptive
The immigrant's operator answered, while ensureing to solve quality, also maintains the diversity of population.
4th, the process velocity of the invention based on batch processing time and equipment devises the adjustable strategies of particle position；It will set
It is standby to be ranked up by the non-increasing of completion date, then by batch in the longer equipment of completion date and the shorter equipment of completion date
It is secondary to be adjusted by constraints, so as to realize that the position of the particle to iteration each time is adjusted, improve the quality understood.
Brief description of the drawings
Fig. 1 enters the method flow diagram of transport production cooperative scheduling for the present invention using particle cluster algorithm；
Fig. 2 is present invention transport and flow chart.
Embodiment
In this embodiment, a kind of transport and production scheduling method based on particle group optimizing, its flow as shown in figure 1,
Be for workpiece volume and the production time is variant, production equipment speed is variant and different transit routes on haulage time have
The transport of difference, procreative collaboration scheduling problem are modeled, and then improving particle cluster algorithm by one kind is solved, so that
The prioritization scheme dispatched to a set of transport production, total operating cost of Target Enterprise is substantially reduced with this, improves corporate operation effect
Rate.Specifically, it is to carry out the n workpiece at workpiece set after batch processing, m platforms is delivered to by haulage vehicle and set
Standby place is produced and processed；The workpiece set that n workpiece is constituted is designated as J={ J1,J2,…,Jj,…,Jn, JjRepresent j-th of work
Part, 1≤j≤n；By j-th of workpiece JjSize be designated as sj, process time be designated as pj；M platform equipment is designated as M={ M1,M2,…,
Mi,…,Mm, MiRepresent i-th equipment, 1≤i≤m；By i-th equipment MiThe speed of processing workpiece is designated as vi, will be from workpiece collection
I-th equipment M is reached at conjunctioniHaulage time be designated as ti；The volume of the volume of haulage vehicle and processing workpiece device is designated as
C；
Transport and production scheduling method based on particle group optimizing are to carry out as follows：
Step 1, the workpiece in workpiece set J is ranked up by the order of process time non-increasing, if process time is identical
Then it is ranked up by the order of the non-increasing of workpiece size, so that the workpiece set J ' after being sorted；
Step 2, the 1st unappropriated workpiece in the workpiece set J ' after sequence be put into can accommodate the 1st it is unallocated
The size and remaining space of workpiece it is minimum batch in, batch remaining space be all workpiece sizes in corresponding to being put into crowd of volume C
The difference of sum；If can not accommodate the size of the 1st unallocated workpiece in the remaining space criticized, generation volume is criticized for the new of C,
And add the 1st unappropriated workpiece in new batch, until all workpiece in workpiece set J ' are all assigned in corresponding batch；
This in batches strategy by process time close workpiece try one's best distribution in same batch, while making the remaining workpiece of equipment to the greatest extent may be used
Can it is small, can largely less batch sum and batch process time.
Step 3, all batches obtained in step 2 are arranged by batch processing time non-increasing, obtain batch processing set B=
{b1,b2,…,bq,…,bl, bqQ-th batch is represented, the process time of q-th batch is designated as p(q), q-th crowd of bqProcessing when
Between p(q)It is by q-th crowd of bqMost long workpiece of middle process time is determined；Lot count is designated as l, Table
Show the smallest positive integral not less than x；Arranged by that will criticize by batch processing time non-increasing, make to be assigned to and processed in every equipment
Be also to be processed by batch processing time non-increasing batch during processing, in the case where there are haulage time,
The stand-by period of machine can so be reduced.
Step 4, the parameters for initializing particle cluster algorithm, including：Total number of particles N, iterations L, greatest iteration time
Number Lmax, crossover probability Pc1And Pc2, 1≤L≤Lmax；And initialize L=1；
Step 5, generation initial population, obtain the initial position of k-th of particle in L generationsAnd initial velocity WithDifference table
Show position and speed of k-th of particle on d dimensions search space in L generations, wherein, 1≤d≤l；1≤k≤N；Producing just
During beginning population, one of individual makes to obtain on batch machine completed earliest by will criticize to be assigned to, other individuals
Produced by random manner, both ensure that the quality of initial population, maintain the diversity of population, also improve the receipts of algorithm
Speed is held back, specifically,
Step 5.1, orderRepresent i-th equipment MiUpper q-th crowd of bqCompletion date, niRepresent i-th equipment MiOn
The quantity criticized of processing, AiFor i-th equipment MiFree time；Initializationni=1, q=1, Ai=0；
Step 5.2, judge whether q ＞ l set up, if so, then represent one particle of generation；Otherwise, step 5.3 is performed；
Step 5.3, utilize formula (1) obtain q-th crowd of bqCompletion date in i-th equipmentSo as to obtain q
Individual crowd of bqCompletion date in m platform equipment：
In formula (1), max { x, y } represents to take the greater in x and y；
Step 5.4, from q-th crowd of bqSetting corresponding to minimum makespan is selected in completion date in m platform equipment
It is standby, equipment min is designated as, by q-th of batch bqTransport and be processed on equipment min；Again by nmin+ 1 is assigned to nmin,It is assigned to Amin, q+1 is assigned to after q, performs step 5.2.
Based on device rate, by calculating q-th crowd of bqCompletion date in m platform equipment, reselection minimum complete man-hour
Between corresponding equipment be used as q-th crowd of bqProcess equipment, so than by q-th crowd of bqIt is assigned in equipment idle at first
It is processed solving result more excellent.
Step 6, the fitness for calculating k-th of particle in L generationsSo as to obtain the part of k-th of particle in L generations
Optimal solutionWherein,Represent that k-th of particle ties up search space in d in L generations
On optimal location；
Step 7, repeat step 6, obtain the locally optimal solution of N number of particle in L generations, and therefrom select maximum adaptation angle value
Corresponding optimal solution is designated as the globally optimal solution in L generationsWhereinRepresent
Whole particle colony ties up the optimal location on search space in d in L generations；
Step 8, the position according to k-th of particle in L generationsAnd speedK-th of L+1 generations is calculated respectively
The position of particleAnd speedSo as to obtain L+1 for the position of N number of particle and speed in population；
Step 8.1, the initial position by k-th of particle in L generationsIt is assigned toWherein,For withVariable with identical meanings, comparesWithWhether the value of middle corresponding element is identical,
, will if identicalThe value of middle corresponding element is set to 0, if it is different, then willMiddle corresponding element value is set toIn it is right
Answer the value of element；So as to obtain renewalUpdateIn the value of each element represent the sequence number of equipment；And it is each
Element is corresponded with each batch in batch processing set B；
Step 8.2, by renewalCriticizing corresponding to the element of middle non-zero value is dispatched to setting representated by non-zero value successively
It is standby to be above processed, and calculate the completion date of relevant device；So as to obtain equipment completion date set C '=C ' [1] ...,
C ' [i] ..., C ' [m] } and batch size set n '={ n '1,…,n′i,…,n′m}；C ' [i] represents i-th equipment MiIt is complete
Between man-hour, n 'iRepresent i-th equipment MiThe quantity criticized of upper processing；
Step 8.3, by n 'i+ 1 is assigned to n 'i, C ' [i] is assigned to A 'i, wherein, variables A 'iWith AiWith identical meanings；
Step 8.4, by renewalMiddle all values are the batch corresponding to 0 element, by batch processing time non-increasing
Arrangement obtains crowd set B '={ b '1,…,b′q′,…,b′l′, wherein, what l ' expressions updatedIntermediate value is total for 0 element
Number, i.e., unappropriated batch total；
Step 8.5, make q '=1；
If step 8.6, q ' ＞ l ', represent to complete to renewalUpdated again, and perform step 8.9；It is no
Then, step 8.7 is performed；
Step 8.7, formula (2) is utilized to obtain q ' individual batch b 'q′Completion date in i-th equipmentSo as to obtain
Obtain the individual batch b ' of q 'q′Completion date in m platform equipment：
Step 8.8, from the individual batch b ' of q 'q′Selected in completion date in m platform equipment corresponding to minimum makespan
Equipment, equipment min ' is designated as, by the individual batch b ' of q 'q′Equipment min ' is transported above to be processed；Again by n 'min′+ 1 is assigned to
n′min′,It is assigned to A 'min′, q '+1 is assigned to q ', performs step 8.6；
Step 8.9, it is distributed by bernoulli and obtains the l dimension groups R that is made up of 0 and 11, array R1In each element
With again updatingIn each element correspond；If R1In element be 1, then update againMiddle corresponding element
The value of element keeps constant, if R1In element be 0, then update againThe value of middle corresponding element is set to 0；
Step 8.10, by what is updated againIt is used as renewalAnd execution in step 8.2- steps 8.8 is substituted into, from
And obtain after third time renewal
Step 8.11, generalAsWillAsAnd execution in step 8.1- steps 8.10 is substituted into, from
And obtain after third time renewal
Step 8.12, will third time update afterAfter being updated with third timeWith probability Pc1Carry out bernoulli friendship
Fork, the position of k-th of particle in the L generations after being intersected
Step 8.13, generalWithWith probability Pc1Bernoulli intersection is carried out, k-th of particle in L+1 generations is obtained
Speed
Step 8.14, generalWithWith probability Pc1Bernoulli intersection is carried out, k-th of particle in L+1 generations is obtained
Position
Step 8.15, generalWith globally optimal solutionWith probability Pc2Bernoulli intersection is carried out, is obtained after two intersections
Particle, relatively prechiasmal particleWith intersect after two particles fitness, regard the big particle of fitness as
K-th of particle in L+1 generationsPosition；By carrying out fitness comparison to the particle before and after intersection, it is ensured that will produce
The high particle of fitness value remain, so as to improve the quality of population.
Step 8.16, calculate L+1 for each particle in population fitness, fitness is minimumIndividual particle is replaced with the particle randomly generated,Represent the maximum integer no more than x；So as to update L+
The position of 1 generation population.Pass throughCalculate needs to be replaced with the particle randomly generated in the population of iteration each time
The number of particles in generation, the formula increases with iterations L, and the number of particles that the particle being randomly generated is replaced is with iteration time
Number increase gradually increase, thus can iteration early stage increase Local Search ability, make in the iteration later stage population have compared with
Big diversity, it is to avoid be absorbed in local optimum.
Step 9, adjustment L+1 for N number of particle in population position so that N number of grain in the L+1 generations after being adjusted
The position of son；
Step 9.1, defined variable f and h, the maximum length for defining Local Search are fmax；Make f=1, i=1, h=m；
Step 9.2, judge f ＜ fmaxWhether set up, if so, then m platforms equipment is arranged by the non-increasing of completion date
Row, perform step 9.3；Otherwise, L+1 is completed for N number of particle position in populationAdjustment；
Step 9.3, i-th equipment M of selectioni；Select h platform equipment Mh；
Step 9.4, judge whether h ＞ 1 set up, if so, then perform step 9.5；Otherwise, L+1 is completed for population
In N number of particle positionAdjustment；
Step 9.5, any α crowd of b of searchingα, bα∈Mi；And any β crowd bβ, bβ∈Mh；If α crowd bα
With β crowd bβFormula (3) is met, then exchanges α crowd bαWith β crowd bβ, and perform step 9.6；Otherwise, step is performed
9.7：
In formula (3), p(α)Represent α crowd bαProcess time, C [i] represent i-th equipment MiCompletion date, p(β)Table
Show β crowd bβProcess time, C [h] represents h platform equipment MhCompletion date；Criticizing for formula (3) will be met to swap
The completion date of completion date most long equipment can be shortened, and will not surpass the completion date of the less equipment of completion date
The completion date of completion date most long equipment before exchanging is crossed, therefore the exchange can shorten manufacture span.
Step 9.6, batch being arranged by batch process time non-increasing in same equipment will be assigned to, and calculate individual device
Completion date, f is assigned to by f+1, performs step 9.2；
Step 9.7, h-1 is assigned to after h, performs step 9.4.
Step 10, calculating L+1 are for k-th of particle in populationFitnessAnd with L k-th in
The fitness of particleIt is compared, regard the corresponding particle position of larger fitness value as k-th particle in L+1 generations
Optimal solution
Step 11, repeat step 10, thus obtain L+1 generation in N particles optimal solution and therefrom select maximum adaptation degree
It is worth globally optimal solution of the corresponding optimal solution as L+1 generations
Step 12, L+1 is assigned to L, judges L ＜ LmaxWhether set up, if so, then perform step 8；Otherwise, represented
Into LmaxSecondary iteration, and obtain globally optimal solutionWith globally optimal solutionCorresponding scheduling scheme is adjusted as optimal
Degree scheme, workpiece batching and transport and manufacturing process are as shown in Figure 2.
Claims (4)
1. a kind of transport and procreative collaboration dispatching method based on Modified particle swarm optimization, it is characterized in that, workpiece set will be in
The n workpiece at place is carried out after batch processing, is delivered at m platform equipment and is produced and processed by haulage vehicle；The n workpiece
The workpiece set of composition is designated as J={ J1,J2,…,Jj,…,Jn, JjRepresent j-th of workpiece, 1≤j≤n；By j-th of workpiece Jj
Size be designated as sj, process time be designated as pj；The m platforms equipment is designated as M={ M1,M2,…,Mi,…,Mm, MiI-th is represented to set
It is standby, 1≤i≤m；By i-th equipment MiThe speed of processing workpiece is designated as vi, i-th equipment M will be reached at the workpiece seti
Haulage time be designated as ti；The volume of the volume of haulage vehicle and processing workpiece device is designated as C；
Described transport with procreative collaboration dispatching method is to carry out as follows：
Step 1, the workpiece in the workpiece set J is ranked up by the order of process time non-increasing, if process time is identical
Then it is ranked up by the order of the non-increasing of workpiece size, so that the workpiece set J ' after being sorted；
Step 2, it the 1st unappropriated workpiece in the workpiece set J ' after the sequence is put into can accommodate described 1st not
Distribute workpiece size and remaining space it is minimum batch in, batch remaining space be all workpiece in corresponding to being put into crowd of volume C
The difference of size sum；If can not accommodate the size of the 1st unallocated workpiece in the remaining space criticized, generation volume is C's
New batch, and the 1st unappropriated workpiece is added in new batch, until all workpiece in the workpiece set J ' are all assigned to phase
Answer batch in；
Step 3, all batches obtained in step 2 are arranged by batch processing time non-increasing, obtain batch processing set B={ b1,
b2,…,bq,…,bl, bqQ-th batch is represented, q represents the batch of processing, and the process time of q-th batch is designated as into p(q), q-th
Criticize bqProcess time p(q)It is by q-th crowd of bqMost long workpiece of middle process time is determined；Lot count is designated as l, Represent the smallest positive integral not less than x；
Step 4, the parameters for initializing particle cluster algorithm, including：Total number of particles N, iterations L, maximum iteration
Lmax, crossover probability Pc1And Pc2, 1≤L≤Lmax；And initialize L=1；
Step 5, generation initial population, obtain the initial position of k-th of particle in L generationsAnd initial velocity WithDifference table
Show position and speed of k-th of particle on d dimensions search space in L generations, wherein, 1≤d≤l；1≤k≤N；
Step 6, the fitness for calculating k-th of particle in L generationsSo as to obtain the local optimum of k-th of particle in L generations
SolutionWherein,Represent that k-th of particle is on d dimensions search space in L generations
Optimal location；
Step 7, repeat step 6, obtain the locally optimal solution of N number of particle in L generations, and therefrom select maximum adaptation angle value correspondence
Optimal solution as the globally optimal solution in L generations, be designated asWhereinRepresent L
Whole particle colony ties up the optimal location on search space in d in generation；G represents L for fitness value maximum in N number of particle
Particle；
Step 8, the position according to k-th of particle in L generationsAnd speedK-th of particle in L+1 generations is calculated respectively
PositionAnd speedSo as to obtain L+1 for the position of N number of particle and speed in population；
Step 9, the adjustment L+1 for N number of particle in population position so that N number of grain in the L+1 generations after being adjusted
The position of son；
Step 10, calculating L+1 are for k-th of particle in populationFitnessAnd with L generation in k-th of particle
FitnessBe compared, using the corresponding particle position of larger fitness value as in L+1 generations k-th particle it is optimal
Solution
Step 11, repeat step 10, thus obtain L+1 generation in N particles optimal solution and therefrom select maximum adaptation angle value pair
The optimal solution answered as L+1 generations globally optimal solution
Step 12, L+1 is assigned to L, judges L ＜ LmaxWhether set up, if so, then perform step 8；Otherwise, represent to complete
LmaxSecondary iteration, and obtain globally optimal solutionWith globally optimal solutionCorresponding scheduling scheme is used as optimal scheduling
Scheme.
2. the production scheduling method according to claim 1 based on particle group optimizing, it is characterised in that the step 5 is created
During building initial population, N-1 particle is produced by random fashion, and a remaining particle is to produce as follows
It is raw：
Step 5.1, orderRepresent i-th equipment MiUpper q-th crowd of bqCompletion date, niRepresent i-th equipment MiUpper processing
Batch quantity, AiFor i-th equipment MiFree time；Initializationni=1, q=1, Ai=0；
Step 5.2, judge whether q ＞ l set up, if so, then represent one particle of generation；Otherwise, step 5.3 is performed；
Step 5.3, utilize formula (1) obtain q-th crowd of bqCompletion date in i-th equipmentSo as to obtain q-th batch
bqCompletion date in m platform equipment：
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In formula (1), max { x, y } represents to take the greater in x and y；
Step 5.4, from q-th crowd of bqThe equipment corresponding to minimum makespan, note are selected in completion date in m platform equipment
For equipment min, by q-th of batch bqTransport and be processed on equipment min；Again by nmin+ 1 is assigned to nmin,Assignment
To Amin, q+1 is assigned to after q, performs step 5.2.
3. the production scheduling method according to claim 1 based on particle group optimizing, it is characterised in that the step 8 is
Carry out as follows：
Step 8.1, the initial position by k-th of particle in L generationsIt is assigned to
Wherein,For withVariable with identical meanings, comparesWithWhether the value of middle corresponding element is identical, if phase
Together, then willThe value of middle corresponding element is set to 0, if it is different, then willMiddle corresponding element value is set toMiddle corresponding element
The value of element；So as to obtain renewalUpdateIn the value of each element represent the sequence number of equipment；And each element
Corresponded with each batch in batch processing set B；
Step 8.2, by renewalCriticizing corresponding to the element of middle non-zero value is dispatched in the equipment representated by non-zero value successively
It is processed, and calculates the completion date of relevant device；So as to obtain equipment completion date set C '=C ' [1] ..., C '
[i] ..., C ' [m] } and batch size set n '={ n '1,…,n′i,…,n′m}；C ' [i] represents i-th equipment MiCompletion
Time, n 'iRepresent i-th equipment MiThe quantity criticized of upper processing；
Step 8.3, by n 'i+ 1 is assigned to n 'i, C ' [i] is assigned to A 'i, wherein, variables A 'iWith AiWith identical meanings；
Step 8.4, by renewalMiddle all values are the batch corresponding to 0 element, are arranged by batch processing time non-increasing
Obtain crowd set B '={ b '1,…,b′q′,…,b′l′, wherein, what l ' expressions updatedIntermediate value is the total number of 0 element,
I.e. unappropriated batch total；
Step 8.5, make q '=1；
If step 8.6, q ' ＞ l ', represent to complete to renewalUpdated again, and perform step 8.9；Otherwise, hold
Row step 8.7；
Step 8.7, formula (2) is utilized to obtain q ' individual batch b 'q′Completion date in i-th equipmentSo as to obtain the
The individual batch b ' of q 'q′Completion date in m platform equipment：
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Step 8.8, from the individual batch b ' of q 'q′Setting corresponding to minimum makespan is selected in completion date in m platform equipment
It is standby, equipment min ' is designated as, by the individual batch b ' of q 'q′Equipment min ' is transported above to be processed；Again by n 'min′+ 1 is assigned to
n′min′,It is assigned to A 'min′, q '+1 is assigned to q ', performs step 8.6；
Step 8.9, it is distributed by bernoulli and obtains the l dimension groups R that is made up of 0 and 11, array R1In each element with again
Secondary renewalIn each element correspond；If R1In element be 1, then update againMiddle corresponding element
Value keeps constant, if R1In element be 0, then update againThe value of middle corresponding element is set to 0；
Step 8.10, by what is updated againIt is used as renewalAnd execution in step 8.2- steps 8.8 is substituted into, so as to obtain
Obtain after third time renewal
Step 8.11, generalAsWillAsAnd execution in step 8.1- steps 8.10 is substituted into, so as to obtain
Obtain after third time renewal
Step 8.12, will third time update afterAfter being updated with third timeWith probability Pc1Bernoulli intersection is carried out,
The position of k-th of particle in the L generations after being intersected
Step 8.13, generalWithWith probability Pc1Bernoulli intersection is carried out, the speed of k-th of particle in L+1 generations is obtained
Step 8.14, generalWithWith probability Pc1Bernoulli intersection is carried out, the position of k-th of particle in L+1 generations is obtained
Step 8.15, generalWith globally optimal solutionWith probability Pc2Bernoulli intersection is carried out, the grain after two intersections is obtained
Son, relatively more prechiasmal particleWith intersect after two particles fitness, regard the big particle of fitness as L+1
K-th of particle in generationPosition；
Step 8.16, calculate L+1 for each particle in population fitness, fitness value is minimum
Individual particle is replaced with the particle randomly generated,Represent the maximum integer no more than x；So as to update positions of the L+1 for population
Put.
4. the production scheduling method according to claim 1 based on particle group optimizing, it is characterised in that the step 9 is
It is adjusted as follows：
Step 9.1, defined variable f and h, the maximum length for defining Local Search are fmax；Make f=1, i=1, h=m；
Step 9.2, judge f ＜ fmaxWhether set up, if so, then m platforms equipment is arranged by the non-increasing of completion date, held
Row step 9.3；Otherwise, L+1 is completed for N number of particle position in populationAdjustment；
Step 9.3, i-th equipment M of selectioni；Select h platform equipment Mh；
Step 9.4, judge whether h ＞ 1 set up, if so, then perform step 9.5；Otherwise, L+1 is completed for N number of in population
Particle positionAdjustment；
Step 9.5, any α crowd of b of searchingα, bα∈Mi；And any β crowd bβ, bβ∈Mh；If α crowd bαWith β
Individual crowd of bβFormula (3) is met, then exchanges α crowd bαWith β crowd bβ, and perform step 9.6；Otherwise, step 9.7 is performed：
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In formula (3), p(α)Represent α crowd bαProcess time, C [i] represent i-th equipment MiCompletion date, p(β)Represent the
β crowd bβProcess time, C [h] represents h platform equipment MhCompletion date,
Step 9.6, batch being arranged by batch process time non-increasing in same equipment will be assigned to, and calculate the completion of individual device
Time, f+1 is assigned to f, performs step 9.2；
Step 9.7, h-1 is assigned to after h, performs step 9.4.
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