CN106959675A - A kind of multi-objective scheduling optimization method towards Flow Shop - Google Patents
A kind of multi-objective scheduling optimization method towards Flow Shop Download PDFInfo
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Abstract
The invention discloses a kind of multi-objective scheduling optimization method towards Flow Shop, the problem of it solves the multiple target production scheduling model adaptability to changes deficiency for setting up Flow Shop in the prior art, with that can adjust the effect of production scheduling strategy according to the change of enterprise's Production requirement at any time, its technical scheme is：Towards Flow Shop, the object and multi object mathematical model of production running time, production cost and the workshop energy consumption in workshop is established, Workshop Production optimizing scheduling is carried out with normalizing method of weighting；According to enterprise demand, by each index weights of analytic hierarchy process (AHP) decision-making, optimizing iteration is carried out to this problem using genetic algorithm, optimizing scheduling scheme is obtained.
Description
Technical field
The present invention relates to Job-Shop technical field, more particularly to a kind of multi-objective scheduling optimization side towards Flow Shop
Method.
Background technology
Flow Shop Scheduling can be described generally as：A collection of workpiece set to be processed, in a certain order according to
Secondary to be processed by specific system of processing, each work pieces process process is identical, and every lathe one procedure of correspondence, is meeting
While certain constraints, by dispatching work pieces process sequentially, reasonable distribution system resource so that some indexs are optimal.Stream
Water Job-Shop has a wide range of applications as typical production operation pattern in discrete manufacturing business, therefore to Flow Shop
Scheduling problem expansion research have critically important theory and engineering application value.
At present, a large amount of scholars establish corresponding production model and then to Workshop Production from different production angles
Problems of Optimal Dispatch is solved.But it is scheduled, is such as based on for fixed single or multiple optimizing index more than these models
The Job-Shop of production efficiency, the Job-Shop based on cost and energy consumption etc., with stronger selectivity and specific aim.With society
It can improve, the aggravation of the market competitive pressure in the actual production process of workshop, often faces enterprises production efficiency and urgently lifted, passed through
The increase of benefit of helping demand and the pressure of increasingly serious etc. each side of environmental problem, workshop is needed at any time according to enterprise external demand
Select corresponding production strategy.
In summary, in the prior art for how to strengthen flow shop scheduling adaptability to changes the problem of, still lack
Effective solution.
The content of the invention
In order to overcome the deficiencies in the prior art, the invention provides a kind of multi-objective scheduling optimization side towards Flow Shop
Method, with efficient, economy, the effect of energy-conservation, according to enterprise demand, by each index weights of analytic hierarchy process (AHP) decision-making, utilizes something lost
Propagation algorithm carries out optimizing iteration to this problem, obtains optimizing scheduling scheme.
The present invention uses following technical proposals：
A kind of multi-objective scheduling optimization method towards Flow Shop, comprises the following steps：
Step 1：The production process of Flow Shop is divided into processing, wait, adjustment and transport four-stage；
Step 2：The running time of Flow Shop is divided into process time, stand-by period, adjustment time and haulage time, sets up
The running time model of Flow Shop；
Step 3：By the totle drilling cost consumed in Flow Shop production process be divided into machine tooling cost, lathe delay cost,
Setting for machine cost and workpiece transport cost, set up the total cost model of Flow Shop；
Step 4：Flow Shop energy consumption is divided into lathe energy consumption, transport energy consumption and public auxiliary energy consumption, Flow Shop is set up
Total energy consumption model；
Step 5：The multi-objective Model of time, cost, energy consumption is converted into single goal production Operation Model；
Step 6：The single goal production Operation Model of Flow Shop is solved.
Further, will processing, adjustment lathe behavior corresponding with loitering phase, haulage stage correspondence in described step 1
Behavior of the workpiece between different lathes.
Further, process time PT in described step 2totalTo process workpiece since lathe after workpiece is reached to complete
Into the time shared by process requirements；Stand-by period WTtotalDuring for machine tool processing workpiece, production is waited because workpiece does not reach lathe
Raw standby time；Adjustment time ATtotalIt is next work pieces process to terminate rear lathe to change cutter, fixture for a upper work pieces process
The time for occupancy of preparing；Haulage time TTtotalFor workpiece to be transported to the time of lower a machine tool from upper a machine tool.
Further, the totle drilling cost consumed in the production process described in described step 3 also includes lathe running
In power consumption, ignore setting for machine time-bands come cost consumption；The totle drilling cost C of i.e. described Flow Shop is machine tooling
Cost Pcost, lathe delay cost Wcost, workpiece transport cost TcostWith workshop energy consumption cost EcostSum.
Further, lathe energy consumption includes machine tooling energy consumption in production operation, lathe wait in described step 4
Energy consumption and setting for machine energy consumption；Described transport energy consumption includes workshop and transports the energy that each workpiece is produced to corresponding production equipment
Consumption.
Further, described machine tooling energy consumption PE is the energy that each lathe is produced during being processed to workpiece
Amount consumption；Described lathe waits the idle energy consumption that energy consumption WE produces for lathe because workpiece is not reached；Described adjustment energy
Consumption AE changes the energy ezpenditure that fixture, cutter process are produced after terminating for a workpiece in machine tooling；Described transport energy consumption TE
For workpiece is transported into the energy ezpenditure produced during lower a machine tool is processed by upper a machine tool；Described is public
Auxiliary energy consumption PCE represents the energy ezpenditure that infrastructure operation in workshop is produced in whole process, mainly including billboard, illumination
The energy consumption of the infrastructure such as lamp, ventilating fan, and measured more with electricity.
Further, time weighting is represented with α in described step 5, β represents cost weight, and γ represents energy consumption weight, with
fnorm(x) normalized function is represented, single goal production Operation Model is expressed as：
min Zobject=α fnorm(T)+β·fnorm(C)+γ·fnorm(E)
S.t. alpha+beta+γ=1
T≤T0
E≤E0
C≤C0
Wherein, T represents Workshop Production total time；C represents Workshop Production totle drilling cost；E represents workshop total energy consumption；T0Represent car
Between process time constrain；E0Represent the constraint of workshop power consumption of polymer processing；C0Represent the constraint of workshop processing cost.
Further, assignment is carried out to the weight of time, cost, energy consumption using analytic hierarchy process (AHP).First according to 9 scaling laws
Index weights are given a mark, by the judgment matrix of generation, each several part weight are calculated according to geometric average method, by uniformity
After inspection, suitable weight coefficient is obtained.
Further, optimizing iteration is carried out to production Operation Model using genetic algorithm in described step 6, obtains workpiece
Optimal scheduling scheme.
Further, described iterative step includes：
(1) encode；(2) chromosome population is initialized；(3) fitness function；(4) Rule of judgment；(5) selection opertor；(6)
Crossover operator；(7) mutation operator.
Compared with prior art, the beneficial effects of the invention are as follows：
(1) present invention is towards Flow Shop, has considered time of Workshop Production process, cost, energy consumption tripartite
Face content, based on normalizing method of weighting, is converted to single goal production Operation Model by multiple objective function, is determined by analytic hierarchy process (AHP)
Plan obtains each index weights, and optimal processing scheme is obtained with GA algorithm optimizations；
(2) according to Flow Shop Production requirement, comprehensive, efficient, energy-conservation, economic four kinds of typical production models is proposed, are led to
Cross expert and the index weights for obtaining each production model are calculated according to the judgment matrix of Method of nine marks marking generation；Enterprise can be according to car
Between Production requirement, the weight for selecting corresponding modes according to sensitivity analysis is scheduled optimization, the tune that acquisition tallies with the actual situation
Degree scheme；
(3) present invention is by taking the processing scheduling of four parts of Flow Shop gear oil pump as an example, and the application for having carried out model is tested
Card；In analysis of cases, influence of the weight coefficient to optimum results is further study using susceptibility assays, is enterprise pair
The selection and decision-making of production model provide foundation；
(4) research object of the invention is identical for each part manufacturing procedure, and each operation can only add on a machine tool
The Flow Shop of work, the hybrid flowshop or other workshops that can further have LPT device to each workpiece process
Expanded.
Brief description of the drawings
The Figure of description for constituting the part of the application is used for providing further understanding of the present application, and the application's shows
Meaning property embodiment and its illustrate be used for explain the application, do not constitute the improper restriction to the application.
Fig. 1 produces operation figure for the Flow Shop of the present invention；
Fig. 2 is analytic hierarchy process (AHP) flow chart of the invention；
Fig. 3 is Flow Shop gear oil pump front view of the invention；
Fig. 4 is Flow Shop gear oil pump side view of the invention；
Fig. 5 is the production task Gantt chart under aggregative model of the present invention；
Fig. 6 is the production task Gantt chart under high effective model of the present invention；
Fig. 7 is the production task Gantt chart under energy saver mode of the present invention；
Fig. 8 for the present invention efficiently with each energy consumption comparison figure of energy saver mode；
Fig. 9 is the production task Gantt chart under cost pattern of the present invention；
Figure 10 is that energy-conservation of the present invention is schemed with each Cost comparisons of economic model；
Figure 11-Figure 13 is respectively the weight α of the present invention, β, γ to the influence curve of object function.
Embodiment
It is noted that described further below is all exemplary, it is intended to provide further instruction to the application.Unless another
Indicate, all technologies used herein and scientific terminology are with usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singulative
It is also intended to include plural form, additionally, it should be understood that, when in this manual using term "comprising" and/or " bag
Include " when, it indicates existing characteristics, step, operation, device, component and/or combinations thereof.
As background technology is introduced, there is the multiple target production scheduling mould set up for Flow Shop in the prior art
The deficiency of type adaptability to changes, in order to solve technical problem as above, present applicant proposes a kind of multiple target towards Flow Shop
Method for optimizing scheduling.
There is provided a kind of multi-objective scheduling optimization side towards Flow Shop in a kind of typical embodiment of the application
Method, comprises the following steps：
Step 1：The production process of Flow Shop is divided into processing, wait, adjustment and transport four-stage；
Step 2：The running time of Flow Shop is divided into process time, stand-by period, adjustment time and haulage time, sets up
The running time model of Flow Shop；
Step 3：By the totle drilling cost consumed in Flow Shop production process be divided into machine tooling cost, lathe delay cost,
Setting for machine cost and workpiece transport cost, set up the total cost model of Flow Shop；
Step 4：Flow Shop energy consumption is divided into lathe energy consumption, transport energy consumption and public auxiliary energy consumption, Flow Shop is set up
Total energy consumption model；
Step 5：The multi-objective Model of time, cost, energy consumption is converted into single goal production Operation Model；
Step 6：The single goal production Operation Model of Flow Shop is solved.
First, Flow Shop Scheduling is modeled
Processing, the incidence relation for waiting, adjusting between transport four-stage are as shown in Figure 1.Wherein lathe is used as workshop
The critical equipment of production, occupies critical role in production scheduling, therefore will processing, adjustment lathe row corresponding with loitering phase
For behavior of the haulage stage correspondence workpiece between different lathes.
1st, the Flow Shop production time models
Workpiece total elapsed time reflects production efficiency, and good production efficiency is timely completed production task for workshop and provides guarantor
Barrier.According to Flow Shop productive prospecting, it is assumed that the processing sequence of workpiece is N { j1,j2,j3...ji...jn, car traditionally
Between dispatching method, set up the relevant parameter model of each time.
Wherein：ST(ji,mk) represent beginning process time of i-th of workpiece in kth platform lathe on lathe；FT(ji,mk) table
Show completion process time of i-th of workpiece in kth platform lathe on lathe；WTtotalRepresent machine tool processing workpiece total stand-by period.
Beginning process time ST (j of 1st workpiece in the 1st lathe1,m1) and complete process time FT (j1,m1)：
ST(j1,m1)=0 (1)
In formula：Represent process time of the 1st workpiece in the 1st lathe.
Beginning process time ST (j of 1st workpiece in kth platform lathe1,mk) and complete process time FT (j1,mk), its
Middle k=2,3,4...m
In formula：Represent that the 1st workpiece is transported the haulage time of kth platform lathe by -1 lathe of kth
K=2,3,4...m；Represent process time of the 1st workpiece in kth platform lathe.
Beginning process time ST (j of i-th of workpiece in the 1st lathei,m1) and complete process time FT (ji,m1)：
In formula：Represent adjustment time of i-th of workpiece in the 1st lathe；Represent i-th of workpiece at the 1st
The process time of lathe.
Beginning process time ST (j of i-th of workpiece in kth platform lathei,mk) and complete process time FT (ji,mk), its
In
In formula：Represent adjustment time of i-th of workpiece in kth platform lathe；Represent i-th of workpiece in kth platform
The process time of lathe.
The running time of Flow Shop is divided into by process time, wait to the division of production various stages according to Fig. 1
Time, adjustment time, the part of haulage time four.
Process time PTtotalRepresent since processed after workpiece arrival lathe workpiece to complete shared by process requirements when
Between, it is expressed as：
Stand-by period WTtotalWhen representing machine tool processing workpiece, because workpiece is not reached, during to be generated idle such as lathe
Between, it is expressed as：
Adjustment time ATtotalIt is to process next workpiece to do to represent that a upper workpiece terminates lathe after processing to change cutter, fixture
Prepare the time taken, be expressed as：
Haulage time TTtotalWorkpiece is transported to the time of lower a machine tool by expression from upper a machine tool, is expressed as：
2nd, Flow Shop Cost Modeling
Each stage that the totle drilling cost consumed in Flow Shop production process is divided according to manufacture process represents successively
For machine tooling cost, lathe delay cost, setting for machine cost, workpiece transport cost, it is considered to the power consumption of lathe running
Amount is also that the important component of cost adds workshop energy consumption cost.And the setting for machine time is shorter, ignore adjustment bring into
This consumption, therefore, total energy consumption cost are expressed as follows：
C=Pcost+Wcost+Tcost+Ecost (13)
In formula：C represents workshop totle drilling cost；PcostRepresent workshop machine tooling cost；WcostRepresent that workshop lathe is waited into
This；TcostRepresent workshop workpiece transport cost；EcostRepresent workshop energy consumption cost.
Machine tooling cost PcostRefer to material, artificial, processing charges and the equipment folding of machine tool processing workpiece process consumption
The expenses such as old, administration fee.What machine tooling cost was usually calculated according to hour, different geographical, the processing cost of distinct device
Different, many 10-20 are first per hour for the general cost than machine tool of processing cost of Digit Control Machine Tool.
In formula：UPC(mk) represent unit interval kth platform machine tooling cost.
Lathe delay cost WcostLathe is represented because workpiece is not reached, the cost that the idle cycle that leaves unused is produced.Lathe etc.
Treat that cost is directly proportional to the lathe stand-by period, different lathes wait different.
1) delay cost of the 1st lathe
Wcost1=0 (15)
2) delay cost (k=2,3,4...m) of kth platform lathe
In formula：UWC(mk) represent unit interval kth platform lathe delay cost.
Workpiece transport cost TcostRepresent that workpiece is transported to during lower a machine tool by AGV dollies by upper a machine tool to produce
Raw cost.
1) cost of transportation of the 1st Machinetool workpiece
TC1=0 (17)
2) cost of transportation (k=2,3,4...m) of kth platform Machinetool workpiece
In formula：UTC(mk-1, mk) represent that workpiece is transported to the transport of kth platform lathe by the unit interval by -1 lathe of kth
Cost.
Workshop energy consumption cost EcostRefer to the cost that total energy consumption is produced in whole production process in workshop, measured by power consumption
Amount.According to the situation of China's electricity price for industrial uses, 0.725 yuan/kWh of specific energy consumption cost is taken.
Ecost=(E1000/3600) (0.725) (19)
In formula：E represents that workshop processes total energy consumption.
Workshop totle drilling cost is expressed as：
3rd, Flow Shop energy consumption is modeled
Energy consumption is in turn divided into by car according to Workshop Production operational characteristics：(1) lathe energy consumption is included in production operation
Power consumption of polymer processing, wait energy consumption and the setting for machine energy consumption of lathe；(2) transport energy consumption includes each workpiece of workshop transport to accordingly
The energy consumption that production equipment is produced；(3) public auxiliary energy consumption includes the various public energy that the auxiliary equipment such as workshop billboard, illumination are produced
Consumption；
(1) lathe energy consumption
Power consumption of polymer processing PE represents the energy ezpenditure that each lathe is produced during being processed to workpiece, refers mainly to workpiece
The electric quantity consumption of each lathe when being produced and processed.
In formula：PEUnit(ji,mk) represent the power consumption of polymer processing that i-th of workpiece of unit interval is produced in kth platform lathe；M is represented
Process total number of units of the lathe of workpiece；N represents the total number of workpiece.
Wait energy consumption WE to represent lathe due to the idle energy consumption that workpiece is not reached and is produced, refer mainly to lathe idle running and middle
The energy ezpenditure of some energy storage links.
In formula：WEUnit(mk) represent the wait energy consumption that unit interval kth platform lathe is produced.
Adjustment energy consumption AE represents to change after a workpiece terminates in machine tooling the energy that the processes such as fixture, cutter produce and disappeared
Consumption.
In formula：AEUnit(ji,mk) represent the adjustment energy consumption that i-th of workpiece of unit interval is produced in kth platform lathe.
(2) energy consumption is transported
Transport energy consumption TE, which represents workpiece being transported to during lower a machine tool is processed by upper a machine tool, to be produced
Energy ezpenditure.Refer mainly to the means of transports such as AGV dollies, fork truck, conveyer belt and perform the energy ezpenditure produced during transportation function, it is many
With electricity consumption metric, AGV dollies power consumption is taken as the main source of transport energy consumption.
In formula：TEUnit(mk-1,mk) represent that workpiece is transported to the generation of kth platform lathe by the unit interval by -1 lathe of kth
Transport energy consumption.
(3) public auxiliary energy consumption
Public auxiliary energy consumption PCE represents the energy ezpenditure that infrastructure operation in workshop is produced in whole process, mainly
Include the energy consumption of the infrastructure such as billboard, illuminating lamp, ventilating fan, and measured more with electricity.
PCE=PCEUnit·T (25)
T=F (jn,mm)-S(j1,m1) (26)
In formula：PCEUnitRepresent the public energy consumption of unit interval workshop infrastructure consumption；T represents that workshop processing workpiece is total
Time.
Therefore, the total energy consumption E of Flow Shop is expressed as：
2nd, Flow Shop production Operation Model conversion
The multi-objective Model that enterprise dispatches is converted into monocular by generalized time of the present invention, cost, the demand of the aspect of energy consumption three
Mark production Operation Model, as shown in formula (28).In actual production process, meet casual labourer part completion date contracts workshop at any time
Short, work piece production cost reduces, carried forward vigorously the demand of the suddenly changes such as green production, and traditional Optimal Operation Model can not
Such situation is tackled, the change parameter weight that production Operation Model proposed by the present invention can at any time according to demand sets up symbol
Close the optimal scheduling scheme of actual demand.
In formula：α represents time weighting；β represents cost weight；γ represents energy consumption weight；T represents Workshop Production total time；C
Represent Workshop Production totle drilling cost；E represents workshop total energy consumption；T0Represent the constraint of workshop process time；E0Represent workshop power consumption of polymer processing about
Beam；C0Represent the constraint of workshop processing cost.
fnorm(x) normalized function is represented, is referred to eliminate the influence of dimension between time, cost, three indexs of energy consumption,
The processing method that data are carried out.When data are normalized, each data are mapped in 0~1 and handled, change letter
Number is as follows：
α, β, γ are respectively time, cost, the weight of energy consumption, and for the Weight of three kinds of targets, the present invention is adopted first
The step of carrying out assignment, analytic hierarchy process (AHP) with analytic hierarchy process (AHP) (AHP) is as shown in Figure 2.
Index weights are given a mark according to 9 scaling laws, by the judgment matrix of generation, calculate each according to geometric average method
Fractional weight, after consistency check, obtains suitable weight coefficient.
Different Production requirements are faced in Workshop Production operation, correspondence produces different Job-Shop patterns.This hair
The bright a variety of conditions of production for considering Flow Shop, by workshop mode division be aggregative model, high effective model, energy saver mode,
The typical production model of four kinds of economic model, and AHP decision-makings are carried out to each pattern, the weight under corresponding modes is obtained, specifically such as
Shown in table 1.
Wherein, the time in workshop, cost, the aspect index of energy consumption three are carried out comprehensive grading, each index weights by aggregative model
Difference is smaller, and enterprise focuses on three aspect balanced development；High effective model refers to that the demand of production efficiency preferentially lays particular stress in enterprise, to obtain
The workpiece most short production time is used as top priority；Energy saver mode refers to that energy consumption constrains larger to enterprise production process, and enterprise more notes
The influence that weight production process is produced to environment, will consider index based on environment sex chromosome mosaicism；Economic model refers to enterprise with workpiece
Minimum production cost is demand, more focuses on company interest, regard economy problems as primary goal.
In addition, by carrying out sensitivity analysis to the time for producing Operation Model, cost, three index weights of energy consumption, can
To obtain departure degree of each weight for the result of decision.Weight coefficient sensitivity level is higher, change of the workshop under the index
Change amplitude is bigger, and production model is more obvious.Enterprise can combine demand, and the knowledge of enterprise's scheduling method is carried out according to sensitivity analysis
Not, and then obtain meeting the optimal scheduling scheme of Workshop Production feature.
The typical production pattern of table 1
3rd, the single goal production Operation Model of Flow Shop is solved
Genetic algorithm (Genetic Algorithm) is based on theory of heredity and natural selection, by biological evolution
The efficient optimizing searching algorithm that survival of the fittest rule is combined with colony's random exchanging mechanism of intrinsic stain body information in journey, has
The advantages of multiple-objection optimization, global optimization, higher robustness.The present invention chooses genetic algorithm towards Flow Shop and production is transported
Make model and carry out optimizing iteration, obtain workpiece optimal scheduling scheme.Iterative step is as follows：
(1) encode
The most frequently used coding method at present is binary coding, but such a coding method causes coded strings length when high latitude
Search efficiency is reduced.Integer coding solves problems well, and coding statement is simple, clear understandable, so of the invention
Choose integer coding mode.
(2) chromosome population is initialized
The generation of the chromosome population individual typically initialized is random, without specific producing method, using difference
Chromosome coding mode, initial population is different.
(3) fitness function
The optimization aim of the present invention is respectively T, C, E, it is desirable to which comprehensive three aspect obtains minimum value, directly can give birth to single goal
Produce Operation Model Z=α fnorm(T)+β·fnorm(C)+γ·fnorm(E) it is set to fitness function.
(4) Rule of judgment
Typical conditions determination methods are iterations.Iterations is too small, it is not easy to find optimal solution, and iterations is too many
Then inefficiency, and easily chaotic.
If in the range of Rule of judgment, in order to safeguard genetic algorithm genetic diversity, being carried out using genetic operator to population
Optimizing.Genetic operator is generally divided into selection opertor, crossover operator, mutation operator.
(5) selection opertor
The present invention is taken using wheel disc bet method, and its principle is that the fitness value of individual is equivalent into a wheel disc, each
The regional perspective of individual is determined by the fitness value of individual, randomly generates a real number, real number fall regional choice it is accordingly individual
Body is put into population.Fitness is bigger, then the regional perspective on wheel disc is bigger, and selected probability is bigger.
(6) crossover operator
Crossover operator is by by the portion gene cross exchanged of parent chromosome, forming new offspring individual.But intersecting
During be also easy to produce the duplicate factor for not meeting ordering rule, the method that the present invention is exchanged using illegal gene, by illegal gene
Amendment legalizes.From two point crossover operator, crossover probability is controlled in 0.4-0.99.
(7) mutation operator
Genetic algorithm by after intersection it is possible that Premature convergence, can be to a certain extent using mutation operation
Overcome above-mentioned situation, increase the diversity of population, improve the precision of solution.From Mutation operator, the scope control of variation exists
0.0001-0.1, the gene after variation needs also exist for legalizing by amendment.
It is effective to model by taking each part processing of Flow Shop gear oil pump as an example in the another embodiment of the application
Property and accuracy are verified.In Flow Shop, 4 vital parts of gear oil pump sequentially pass through stream according to certain order
Each lathe is processed shaping on waterline.Time, energy consumption and unit interval machine of each part every lathe correspondence each stage
Bed processing cost, lathe delay cost, workpiece transport cost equal matrix are as shown in table 2.
The piston production run data of table 2
Enterprise can basisIntoCarThisBetween work piece production situation the scope of time, cost, energy consumption is bound, each part life
During production, according to practical condition, the scope of three indexs takes respectively：Time [46,59], cost C ∈ [428,519],
Energy consumption E ∈ [57730,80560].
When carrying out decision-making to each index weights α, β, γ using AHP methods, the different scheduling of different index weights correspondences
Prioritization scheme.This workshop is respectively to aggregative model (balanced each index), high effective model (laying particular stress on the weight time), energy saver mode
(laying particular stress on Beijing South Maxpower Technology Co. Ltd's consumption), economic model (laying particular stress on weight cost) carry out optimizing iteration, and the scheduling solved under each pattern of this job shop is excellent
Change is sequentially.
The present invention weights obtained model Z with normalizingobject=α fnorm(T)+β·fnorm(C)+γ·fnorm(E) conduct
Object function, using genetic algorithm, sets initial population scale 20, iterations is set to 50, and crossover probability is set to 0.7, change
Different probability is set to 0.01, MATLAB programs is run under Cultivation pattern production scheduling problems are solved.Four patterns
The optimizing scheduling result and the specific consumption value of each mode index obtained after analysis is as shown in table 3.
The analysis contrast of the operation result of table 3
As can be seen from Table 3, for aggregative model, three aspect balanced development, the value of its indices is in other patterns pair
Answer between desired value；For high effective model, enterprise can more value the production time, and task is completed as primary goal using the most fast time,
Time most short compared with other schemes is 49min；For energy saver mode, enterprises pay attention is energy-saving, and energy consumption is minimum compared with other schemes
63405KJ；For economic model, enterprise focuses on pursuing benefit, and cost is at least 423.6364 yuan compared with other schemes.Respectively
Production model can have different applications in the case of different Production requirements.
Production task Gantt chart under each pattern of present invention generation clearly gives expression to as shown in Fig. 4, Fig. 5, Fig. 6 and Fig. 8
The time relationship of each workpiece, the characteristics of further being contrasted between each pattern with reference to each achievement data of production workpiece.
Aggregative model be enterprise in process of production, if without particular/special requirement, it is general comprehensive for balance, stable production
Consider each side index, produced according to aggregative model, each time, cost, the value of energy consumption are as shown in table 3.
It is urgent, it is necessary to complete the situation of processing as early as possible that high effective model is applied to production task.For the time, primary variables because
Element is workpiece in each lathe stand-by period and adjustment time.J2 → j3 on lathe M3 is can be seen that from adjustment time matrix, j4 →
J1 adjustment time is much larger than other workpiece, so lathe M3 avoids the tune of the adjustment of workpiece 2 → 3 or workpiece 4 → 1 when processing workpiece
It is whole.High effective model and aggregative model are contrasted, two patterns all avoid the order processing of workpiece 2 → 3,4 → 1 on lathe M3, adjusted
Whole time phase difference less, so the stand-by period turn into obtain high effective model key factor, by contrast aggregative model with efficiently
The production task Gantt chart of pattern can be seen that each work pieces process of high effective model is compact, and total waiting time is considerably less than comprehensive mould
Formula, and the time that result in high effective model by the reduction of stand-by period knowable to optimum results is nearlyer by 4.08% than aggregative model reduction.
Problem of environmental pollution of the energy saver mode mainly for enterprise production process.For energy consumption, primary variables factor is tune
Whole energy consumption, wait energy consumption and public energy consumption, j3 on j1 → j2 on lathe M3, j4 → j3, lathe M1 is drawn from adjustment energy consumption matrix
→ j4 adjustment energy consumption is much larger than other workpiece, so should avoid 3 → 4 adjustment on lathe M1,1 → 2 should be avoided on lathe M3,
4 → 3 adjustment.Energy saver mode and high effective model are contrasted, two patterns all avoid lathe M3 upper 23,14 order processing, from
Unit interval waits energy consumption matrix to show that lathe M4 unit interval waits energy consumption minimum, and lathe M2 unit interval waits energy
Consumption is maximum, about the 5.3 of lathe M2 times, so when lathe M2 is operated, the lathe stand-by period is most short, corresponding lathe M4
The time of upper wait is elongated.The adjustment energy consumption for being computed learning high effective model is about 4.27 times of energy saver mode adjustment energy consumption, public
Energy consumption is more or less the same altogether, and comprehensive each Energy consumption factor, the observable index high effective model energy consumption of energy saver mode reduces 25.96%, time
6% is only increased, energy consumption is changed significantly.
The each several part energy consumption comparison of energy saver mode and high effective model is as shown in fig. 7, by analysis, can obtain processing energy
Consumption, transport energy consumption are immutable, therefore are to reduce production process by reducing wait energy consumption, adjustment energy consumption and public energy consumption
Total energy consumption effective measures.
Economic model is in the way of enterprise is produced premised on obtaining maximum profit.For cost, main influence becomes
Measure as energy consumption cost, delay cost.The unit interval delay cost that can obtain lathe M4 from unit interval delay cost matrix is maximum,
So lathe M4 stand-by period should be less.Economic model and energy saver mode are contrasted, the energy of power consumption mode is tried to achieve by calculating
It is that 12.7691, delay cost is 200 to consume cost, and the energy consumption cost of economic model is that 13.1346, delay cost is 184, so
Comprehensive each cost factor, the cost of economic model reduces by 3.69%, the observable index economy of energy saver mode than the cost of energy saver mode
Pattern reduction by 2.86%.
The each several part Cost comparisons of economic model and energy saver mode are as shown in figure 9, by analysis, processing cost is with transporting into
This is immutable, and processing cost and delay cost account for more than 84.34% in totle drilling cost, and cost of transportation takes second place, energy consumption cost
At least.Therefore it is to reduce the effective ways for processing totle drilling cost by controlling delay cost.
In order to study influence of the weight coefficient to optimum results, the production scheduling feature of the Flow Shop is further recognized,
The present invention sensitivity analysis has been carried out to each weight factor, set respectively Model Weight factor alpha, β, γ span as [0.1,
0.6], MATLAB programs are run, the power for obtaining object function value and drafting as shown in Figure 10-Figure 12 is calculated according to operation result
Influence curve of the weight coefficient to object function.
It is big by the way that this piston workshop weight sensitiveness can be obtained to the influence curve formula analysis that cubic polynomial fitting is obtained
It is small to be followed successively byI.e. this piston workshop scheduling model is most sensitive to coefficient of energy dissipation.With reference to table 3 as can be seen that different moulds
The scheduling scheme contrast of formula, workshop energy consumption amplitude of variation is about 25.96%, it was demonstrated that this workshop energy-saving potential is huge, needs
Pay special attention to influence and change of the energy consumption during Job-Shop.
The preferred embodiment of the application is the foregoing is only, the application is not limited to, for the skill of this area
For art personnel, the application can have various modifications and variations.It is all within spirit herein and principle, made any repair
Change, equivalent, improvement etc., should be included within the protection domain of the application.
Claims (10)
1. a kind of multi-objective scheduling optimization method towards Flow Shop, it is characterised in that comprise the following steps：
Step 1：The production process of Flow Shop is divided into processing, wait, adjustment and transport four-stage；
Step 2：The running time of Flow Shop is divided into process time, stand-by period, adjustment time and haulage time, sets up flowing water
The running time model in workshop；
Step 3：The totle drilling cost consumed in Flow Shop production process is divided into machine tooling cost, lathe delay cost, lathe
Setup Cost and workpiece transport cost, set up the total cost model of Flow Shop；
Step 4：Flow Shop energy consumption is divided into lathe energy consumption, transport energy consumption and public auxiliary energy consumption, the total of Flow Shop is set up
Energy consumption model；
Step 5：The multi-objective Model of time, cost, energy consumption is converted into single goal production Operation Model；
Step 6：The single goal production Operation Model of Flow Shop is solved.
2. a kind of multi-objective scheduling optimization method towards Flow Shop according to claim 1, it is characterised in that described
Step 1 in will processing, adjust corresponding with loitering phase lathe behavior, haulage stage corresponding row of the workpiece between different lathes
For.
3. a kind of multi-objective scheduling optimization method towards Flow Shop according to claim 1, it is characterised in that described
Step 2 in process time PTtotalFor since lathe after workpiece is reached process workpiece to complete process requirements shared by when
Between；Stand-by period WTtotalDuring for machine tool processing workpiece, because workpiece does not reach the standby time to be generated such as lathe；During adjustment
Between ATtotalIt is that next work pieces process is prepared time of occupancy to terminate rear lathe to change cutter, fixture for a upper work pieces process；Fortune
Defeated time TTtotalFor workpiece to be transported to the time of lower a machine tool from upper a machine tool.
4. a kind of multi-objective scheduling optimization method towards Flow Shop according to claim 1, it is characterised in that described
Step 3 described in production process in the totle drilling cost that consumes also include the power consumption in lathe running, ignore lathe tune
The cost consumption that whole time-bands are come；The totle drilling cost C of i.e. described Flow Shop is machine tooling cost Pcost, lathe delay cost
Wcost, workpiece transport cost TcostWith workshop energy consumption cost EcostSum.
5. a kind of multi-objective scheduling optimization method towards Flow Shop according to claim 1, it is characterised in that described
Step 4 in lathe energy consumption include machine tooling energy consumption, lathe in production operation and wait energy consumption and setting for machine energy consumption；Institute
The transport energy consumption stated includes workshop and transports the energy consumption that each workpiece is produced to corresponding production equipment.
6. a kind of multi-objective scheduling optimization method towards Flow Shop according to claim 5, it is characterised in that described
Machine tooling energy consumption PE be the energy ezpenditure that is produced during being processed to workpiece of each lathe；Described lathe is waited
The idle energy consumption that energy consumption WE produces for lathe because workpiece is not reached；Described adjustment energy consumption AE is a workpiece in machine tooling
The energy ezpenditure that fixture, cutter process are produced is changed after end；Described transport energy consumption TE is to transport workpiece by upper a machine tool
It is sent to the energy ezpenditure produced during lower a machine tool is processed.
7. a kind of multi-objective scheduling optimization method towards Flow Shop according to claim 1, it is characterised in that described
Step 5 in time weighting is represented with α, β represents cost weight, and γ represents energy consumption weight, with fnorm(x) normalization letter is represented
Number, single goal production Operation Model is expressed as：
minZobject=α fnorm(T)+β·fnorm(C)+γ·fnorm(E)
S.t. alpha+beta+γ=1
T≤T0
E≤E0
C≤C0
Wherein, T represents Workshop Production total time；C represents Workshop Production totle drilling cost；E represents workshop total energy consumption；T0Represent that workshop adds
Work time-constrain；E0Represent the constraint of workshop power consumption of polymer processing；C0Represent the constraint of workshop processing cost.
8. a kind of multi-objective scheduling optimization method towards Flow Shop according to claim 7, it is characterised in that use
Analytic hierarchy process (AHP) carries out assignment to the weight of time, cost, energy consumption；Index weights are given a mark according to 9 scaling laws, pass through life
Into judgment matrix, according to geometric average method calculate each several part weight, after consistency check, obtain suitable weight system
Number.
9. a kind of multi-objective scheduling optimization method towards Flow Shop according to claim 1, it is characterised in that described
Step 6 in using genetic algorithm to production Operation Model carry out optimizing iteration, obtain workpiece optimal scheduling scheme.
10. a kind of multi-objective scheduling optimization method towards Flow Shop according to claim 9, it is characterised in that institute
The iterative step stated includes：
(1) encode；(2) chromosome population is initialized；(3) fitness function；(4) Rule of judgment；(5) selection opertor；(6) intersect
Operator；(7) mutation operator.
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