CN107330808A - Complex scene scheduling method and system based on quantum evolutionary algorithm - Google Patents
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Abstract
The invention discloses the complex scene scheduling method and system based on quantum evolutionary algorithm, quantum population initialization；Quantum scheduling Fitness analysis：Set up quantum object function, it is considered under the constraints of the production and manufacturing environment of heavy mechanical equipment, quantum object function is solved；The binary system control variable of quantum population generation object function, controls variable according to the binary system of generation, corresponds to the order of work order task, carry out scheduling, and the deadline for calculating whole heavy mechanical equipment work order according to scheduling order is the fitness value of quantum；Compare fitness value, optimal solution is determined by minimum fitness value；Preserve optimal solution；Judge whether to reach termination condition, if not up to, being updated quantum population using Quantum rotating gate；The fitness value that quantum scheduling Fitness analysis step recalculates quantum is returned, continues optimizing.Meet local optimal searching and find optimal solution in the state of relatively balancing with global optimizing, realize that heavy mechanical equipment produces scheduling.
Description
Technical field
Scheduling field is manufactured the present invention relates to the heavy mechanical equipment under the complex scene constrained with storage capacity, especially
It is to be related to the complex scene scheduling method and system based on quantum evolutionary algorithm.
Background technology
Heavy mechanical equipment is manufactured has many particularity compared to standard machinery device fabrication, and heavy mechanical equipment is such as
The motor of the large-scale fan blade or engine production fabrication apparatus thereof of motorbus is expensive, quantity is few and single, to former material
The line side storage capacity stored number level of material is generally number, because special production and manufacturing environment easily produces wire body choking phenomenon,
Having some setbacks for production procedure is caused, so as to cause production efficiency to decline, or even the reduction of product quality is caused.As can be seen here, pin
The urgency and significance of scientific production scheduling are carried out to the heavy mechanical equipment production field of above-mentioned complex scene.
Currently occur be used for produce the mathematical modelings such as genetic algorithm, simulated annealing or the particle cluster algorithm of scheduling,
Its foundation is all based on perfect condition, and the complexity of raw material store tolerance limit system, line side storage capacity and wire body obstructing problem is not considered,
Solve the problems, such as ideal during simple production scheduling, but can not bring actually active for the process of manufacture of heavy mechanical equipment
Directive significance.
The content of the invention
Existing scheduling method is produced for heavy mechanical equipment excessively to idealize, the scheduling time is short, storage capacity raw material is utilized
The low problem of rate, the present invention provides the complex scene scheduling method and system based on quantum evolutionary algorithm, applied to storage capacity
Constraint, wire body obstruction produce scheduling field for the heavy mechanical equipment of the complex scene of representative.
To achieve these goals, the present invention is adopted the following technical scheme that：
Complex scene scheduling method based on quantum evolutionary algorithm, comprises the following steps：
Step (1)：Quantum population is initialized：Including initialization quantum population evolutionary generation, initialization quantum bit coding
With initialization quantum chromosomes quantity；
Step (2)：Quantum scheduling and Fitness analysis：Set up quantum object function, it is considered to the production of heavy mechanical equipment
Under the constraints of manufacturing environment, quantum object function is solved；The binary system control of quantum population generation object function
Variable, variable is controlled according to the binary system of generation, corresponds to the order of work order task, carries out scheduling, is calculated according to scheduling order
The fitness value of the deadline, as quantum of whole heavy mechanical equipment work order；
Step (3)：Compare fitness value, optimal solution is determined by minimum fitness value；Preserve optimal solution；Judge whether to
Up to termination condition, optimal solution is exported if termination condition is reached；Step (4) is jumped to if not up to termination condition；
Step (4)：Quantum updates：Quantum population is updated using Quantum rotating gate；Return to step (2) is recalculated
The fitness value of quantum, continues optimizing.
The heavy mechanical equipment, including：Large-scale blade motor, motorbus engine or hoisting machinery etc., it is used to add
Construction equipment typically purchases one or two because expensive, easily occurs wire body choking phenomenon in process of production.
The complex scene refers to the production and manufacturing environment of heavy mechanical equipment：Produce raw material limited space and line side storehouse
It is several levels to hold stored number, raw material conveying arrangement limited amount and be a several levels；Wire body choking phenomenon takes place frequently.
Quantum population is initialized in the step (1), is comprised the following steps：
Step (1-1)：Initialize evolutionary generation t=0；
Step (1-2)：Initialize quantum bit coding：By determining the selected machine of heavy mechanical equipment work order task
And the order for processing of reaching the standard grade, to obtain more excellent quantum object function.
Need to define two variables in initialization procedure：
First variable represents whether work order i j-th of process processes on k-th of machine, is designated as variable aijkIf,
Select machine k then variable aijkValue is 1, the variable a if non-selected machine kijkValue is 0；
Second variable represents that the processing sequence of work order i j-th of process is l, is designated as variable bijl, when work order i jth
Individual process start to reach the standard grade at l-th of processing sequence processing when, variable bijl1 is designated as, otherwise variable bijlFor 0.
Make A=[a111,a112..., aijk...], whether j-th of process for depositing work order i adds on k-th of machine
The state of work；
Make B=[b111,b112..., bijl...], for whether depositing work order i j-th of process in l-th of processing sequence
Place starts to reach the standard grade the state of processing；
Sequential concatenation is carried out to A and B, the quantum bit coding of multiple heavy mechanical equipment work orders is obtained, is designated as X.
X=A+B=[a111,a112..., aijk,…,b111,b112..., bijl,…] (1)
Wherein, the "+" order of representation splicing in formula (1), aijk、bijl0 is taken respectively or takes 1, X to be binary sequence.
Work order task, process equipment and manufacturing procedure information are contained in binary sequence X by the coded system.
Specifically, a quantum bit position is expressed as：The state representation of quantum bit is：|ψ>=α | 0 ＞+β | 1>,
Wherein | 0 ＞ and | 1 ＞ represents two kinds of basic status of quantum bit；α and β is plural number, represents the general of qubit state
Rate width, α2Probability, the β of state 0 are in for quantum bit2The amplitude and angle of the probability of state 1, α and β are in for quantum bit
Random value, but meet | α |2+|β|2=1；
Q is made to represent chromosome, then the quantum chromosomes that m quantum bit position is represented are
Wherein, | αi|2+|βi|2=1, i=1,2 ..., m；M represents individual chromosome quantum bit bit length, m calculating
Method is as follows：
M=work orders total task number × number of devices+work order total task number2 (3)
Then formula (1) is expressed as with quantum bit：
Step (1-3)：Initialize population：Chromosome population is associated with evolutionary generation t, if the quantum chromosomes kind in t generations
Group be：
Then formula (2) is converted into：
In formula, j represents chromosome number, j=1,2 ... n, and n is that chromosome population scale is total chromosome number amount.
In single iteration, chromosome quantitative is more, is more readily obtained globally optimal solution, but calculating speed is slow.
Chromosome quantitative is fewer, and calculating speed is faster, but is easily trapped into locally optimal solution.
In general, chromosome quantitative is arranged between 40-100, and the present invention takes n=60.
Then formula (4) is converted into after being associated with evolutionary generation t：
Quantum fitness is quantum object function in the step (2), the scheduling strategy used under limited space scene
For positive row's strategy, it is expected that the order of processing is delivered goods as early as possible.
Quantum object function：
Minf (X)=Min ∑sAll work orders(heavy mechanical equipment work order planned end time-heavy mechanical equipment work order plan
Time started) (8)
X is referred to as the binary system control variable of object function, is one group of binary sequence, the quantum target function value is smaller
Better.
Quantum fitness calculate be by according to individual chromosome value obtain work order task each machine reach the standard grade processing when
Between and downtime, also referred to as work order task order according to given by chromosome carries out the process of scheduling.
Numerical value one work order task of correspondence in individual chromosome in chromosome population per dimension, each chromosome bag
The current order information for needing to be arranged work order task is contained.
Process time is determined by the machine models or warehouse compartment type and the work order product attribute of work order task processed, is processed
Time is the Given information of scheduling.
The calculating of quantum object function needs to consider the pact of the production and manufacturing environment of heavy mechanical equipment in the step (2)
Beam condition.
The constraints is specially：During selection machine determines work order process time, big machinery is considered
Priority logical relation of the equipment work order task in work order process, work order task is once can not interrupt, i.e., single work
Single task can not in two times be processed across the unavailable time of machine, and two warehouse compartments can not be also assigned in a process gap and are kept in,
The single machine same time can only process a work order task, and machine only processes workpiece within the available period.
The constraints is expressed as follows by formula：
In formula (9), aijkRepresent work order i j-th of process whether the state being processed in k-th of machine；bijlTable
Show work order i j-th of process whether the state being processed in l processing sequences, machining state takes 0 or takes 1；
Referred to as sequential operator, represents serial number of each element in array,
bijThe institute of work order task processing sequence for depositing i-th of work order, j-th of process is stateful,For array bij
Transposition,Expression takes bijMiddle state is the serial number corresponding to 1 element,Dimension and bijIt is identical；
bi(j+1)The institute of work order task processing sequence for depositing+1 process of i-th of work order jth is stateful,For
Array bi(j+1)Transposition,Expression takes bi(j+1)Middle state is the serial number corresponding to 1 element,'s
Dimension and bi(j+1)It is identical.
First constraint aijk, bijl=0 or 1, all i, j, k and l is set up, it is ensured that chromosome coding it is feasible
Property；
Second constraintIt ensure that work order task order is appointed according to work order
The logical order of business processing is processed；
3rd constraintAll work order task orders are set up, it is ensured that all work orders
Task is only processed once；
4th constraintAll i and j are set up, it is ensured that single work order task is only in a machine
It is processed on device.
The Fitness analysis of the step (2) is controlled to become by quantum chromosomes population Q (t) binary systems for generating object function
Measure X,
Obtained by formula (7) with formula (10),
In formula (11)A binary bit in variable is controlled for binary system,Value：
Wherein γ is random number, takes the arbitrary value in [0,1].
Variable X is controlled according to the binary system of generation, the order of work order task is corresponded to, scheduling is carried out, according to scheduling order
The deadline for calculating whole heavy mechanical equipment work order is the fitness value of quantum.
The scheduling principle：Scheduling comprising inventory limitation scene uses the plug hole scheduling method with time window.In scheduling
During, a dimension will be regarded the time as, regard all occupied machine resources, each warehouse compartment and conveying arrangement as appearance
Device, every machine, warehouse compartment and conveying arrangement are with an available time window in finite time.According to the actual feelings of production line
Condition, increases the storage process in line side storehouse between two-step, regard available machines used as line side Kuku position, line side storehouse storage process institute
An insignificant time interval, such as 1 second are set to the time.Increase corresponding work order according to the difference of work order simultaneously to appoint
Business.
The time window that original production can be put in storage is started shooting or processed according to the machine of producing line calendar and initialized, warehouse compartment
According to pot life initialization time window, time window includes one or more pot lifes section, the machine in each pot life section
Device or warehouse compartment are used for processing or depositing a workpiece.
Scheduling step is as follows in the step (2)：
Step (201)：Quantum is decoded：Incoming single quantum is resolved to the scheduling order of work order task；
Step (202)：Scheduling queue L0 is treated in foundation, and the order deposit of decoded workman's task scheduling is treated into scheduling queue L0
In；
Step (203)：Judgement treats whether scheduling queue L0 is empty, if so, then scheduling is finished, calculates fitness value, terminates；
If it is not, the head work order task for treating scheduling queue L0 is then taken, into step (204)；
Step (204)：Whether judge current work order task is assembling work order；If being put into step (205)；Just enter if not
Enter step (206)；
Step (205)：Judge whether workpiece needed for specifying warehouse compartment is complete, if being put into step (206)；It is put into if not
Step (207)；
Step (206)：Whether judge possible schedule fragment is empty；Determine that if not it is available and its and start process time, more
New possible schedule fragment set；Foundation is had been lined up arranging L1, and current drained work order task is put into L1 afterbody, return to step
(203)；If so, being put into step (208)；
Step (207)：Judgement treats whether scheduling queue L0 is empty, if so, then scheduling fails, fitness value is set to positive nothing
Thoroughly, terminate；Just take if not and treat that scheduling queue L0 head work order task, for current work order task, then appoints the work order just handled
Business is put into the head position for treating scheduling queue L0, return to step (204)；
Step (208)：Whether be empty, if failing with regard to scheduling, fitness value is set to just infinite, terminates if judging L1；If not
The work order task just handled is just put into L0 head positions, it is current work order task to take L1 afterbody work orders task, when updating feasible
Between section set, return to step (206).
The step (3) compares fitness value, and fitness value reckling is correspondingFor optimal solution, optimal solution is preserved to B
(t)；Judge whether to reach termination condition, optimal solution is exported if termination condition is reached；Jumped to if not up to termination condition
Step (4)；
The B (t) is used to preserve population optimal solution, including locally optimal solution.
When step (3) termination condition is the maximum quantum update times set and/or reaches scheduling set in advance
Between desired threshold.
Step (4) quantum updates, and comprises the following steps：
Step (4-1)：T=t+1 is made, X is generated by Q (t-1), according to X binary sequence amount of calculation specific item scalar functions；
Step (4-2)：With Quantum rotating gate Population Regeneration Q (t).
The Quantum rotating gate selects similar Givens orthogonal matrixes form, formula：
In formulaFor the Quantum rotating gate of i-th of quantum bit position of j-th of chromosome that evolutionary generation is t, It is revolving door parameter,For the anglec of rotation, anglec of rotation angular dimension takes fixation
The π of value 0.02, anglec of rotation direction of rotation is determined according to table 1.
The anglec of rotation inquiry table of table 1
In table 1A binary bit in variable is controlled for binary system,To be stored in the optimal solution in B (t)
The binary bit of correspondence position.
The Quantum rotating gate more new formula is：
Quantum bit after renewal needs to meet,
Step (4-3)：Return to step (2) calculates the fitness value of quantum after renewal, by the X corresponding to minimum fitness value
Preserve to B (t).
Complex scene product plan based on quantum evolutionary algorithm, including：Memory, processor and it is stored in memory
Computer program that is upper and running on a processor, following steps are realized described in the computing device during computer program：
Step (1)：Quantum population is initialized：Including initialization quantum population evolutionary generation, initialization quantum bit coding
With initialization quantum chromosomes quantity；
Step (2)：Quantum scheduling and Fitness analysis：Set up quantum object function, it is considered to the production of heavy mechanical equipment
Under the constraints of manufacturing environment, quantum object function is solved；The binary system control of quantum population generation object function
Variable, variable is controlled according to the binary system of generation, corresponds to the order of work order task, carries out scheduling, is calculated according to scheduling order
The fitness value of the deadline, as quantum of whole heavy mechanical equipment work order；
Step (3)：Compare fitness value, optimal solution is determined by minimum fitness value；Preserve optimal solution；Judge whether to
Up to termination condition, optimal solution is exported if termination condition is reached；Step (4) is jumped to if not up to termination condition；
Step (4)：Quantum updates：Quantum population is updated using Quantum rotating gate；Return to step (2) is recalculated
The fitness value of quantum, continues optimizing.
Beneficial effects of the present invention：
1st, evolution algorithm is applied to heavy mechanical equipment production scheduling field, meets local optimal searching and is relatively being put down with global optimizing
Optimal solution is found in the state of weighing apparatus, realizes that heavy mechanical equipment produces scheduling.
2nd, the scheduling of heavy mechanical equipment uses quantum evolutionary algorithm, and input data is changed into quantum information, the algorithm
Deployment process is easy to be transplanted to quantum computer field, on quantum calculation machine platform, and the arithmetic speed of algorithm has novelty
Improve.
3rd, the present invention is not based on perfect condition model, under storage capacity constraints, deployment line side storehouse, conveying arrangement etc.
Complicated production scene fitting actual production, has to actual production by the heavy mechanical equipment production scheduling of above-mentioned realization and refers to
Lead meaning.
4th, the present invention proposes a kind of innovative quantum bit coding method, by work order task, process equipment and processing work
Sequence is encoded simultaneously；The environment such as storage capacity constraints and transport are taken into account, a kind of scheduling suitable for heavy mechanical equipment producing line is proposed
Step.
Brief description of the drawings
The algorithm flow for the complex scene scheduling method and system based on quantum evolutionary algorithm that Fig. 1 provides for the present invention
Figure；
The scheduling step for the complex scene scheduling method and system based on quantum evolutionary algorithm that Fig. 2 provides for the present invention.
Embodiment
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
As shown in figure 1, the complex scene scheduling method based on quantum evolutionary algorithm, comprises the following steps：
Step (1) quantum population is initialized；Including initialization quantum population evolutionary generation, initialization quantum bit coding and
Initialize quantum chromosomes quantity；
Step (1-1) initialization evolutionary generation t=0.
Step (1-2) initialization quantum bit coding：By determining the selected machine of heavy mechanical equipment work order task
And the order for processing of reaching the standard grade, to obtain more excellent quantum object function.
Need to define two variables in initialization procedure：
First variable represents whether work order i j-th of process processes on k-th of machine, is designated as variable aijkIf,
Select machine k then variable aijkValue is 1, and variable-value is 0 if non-selected machine k；
Second variable represents that the processing sequence of work order i j-th of process is l, is designated as variable bijl, when work order i jth
Individual process starts the processing variations per hour b that reaches the standard grade at l-th of processing sequenceijl1 is designated as, otherwise variable bijlFor 0.
Make A=[a111,a112..., aijk...], whether j-th of process for depositing work order i adds on k-th of machine
The state of work；
Make B=[b111,b112..., bijl...], for whether depositing work order i j-th of process in l-th of processing sequence
Place starts to reach the standard grade the state of processing；
Sequential concatenation is carried out to A and B, the quantum bit coding of multiple heavy mechanical equipment work orders is obtained, is designated as X.
X=A+B=[a111,a112..., aijk,…,b111,b112..., bijl,…] (1)
Wherein, the "+" order of representation splicing in formula (1), aijk、bijlTake 0 or take 1, then X is binary sequence.
Work order task, process equipment and manufacturing procedure information are contained in binary sequence X by the coded system.
Specifically, a quantum bit position is expressed as：The state representation of quantum bit is：|ψ>=α | 0 ＞+β | 1>,
Wherein | 0 ＞ and | 1 ＞ represents two kinds of basic status of quantum bit；α and β is plural number, represents the general of qubit state
Rate width, α2Probability, the β of state 0 are in for quantum bit2The amplitude and angle of the probability of state 1, α and β are in for quantum bit
Random value, but meet | α |2+|β|2=1；
Q is made to represent chromosome, then the quantum chromosomes that m quantum bit position is represented are
Wherein, | αi|2+|βi|2=1, i=1,2 ..., m；M represents individual chromosome quantum bit bit length, m calculating
Formula is as follows：
M=work orders total task number × number of devices+work order total task number2 (3)
Then formula (1) is expressed as with quantum bit：
Step (1-3)：Initialize population：Chromosome population is associated with evolutionary generation t, if the chromosome population in t generations is：
Then formula (2) is converted into：
J represents chromosome number in formula, and m represents single quantum dye body length, and n is that chromosome population scale is dye
Colour solid total quantity.
In single iteration, chromosome quantitative is more, is more readily obtained globally optimal solution, but calculating speed is slow.
Chromosome quantitative is fewer, and calculating speed is faster, but is easily trapped into locally optimal solution.
In general, chromosome quantitative is arranged between 40-100, and the present invention takes n=60.
Then formula (4) is converted into after being associated with evolutionary generation t：
Quantum scheduling Fitness analysis；The scheduling strategy used under limited space scene expects processing for positive row's strategy
Order deliver goods as early as possible.
The assessment of the quantum object function needs to consider the constraints of the production and manufacturing environment of heavy mechanical equipment.
The quantum object function is：
Minf (X)=Min ∑sAll work orders(heavy mechanical equipment work order planned end time-heavy mechanical equipment work order plan
Time started) (8)
In formula, X is referred to as the binary system control variable of object function, is one group of binary sequence, the quantum object function
Value is the smaller the better.
Quantum fitness calculate be by according to individual chromosome value obtain work order task each machine reach the standard grade processing when
Between and downtime, also referred to as work order task order according to given by chromosome carries out the process of scheduling.
Numerical value one work order task of correspondence in individual chromosome in chromosome population per dimension, each chromosome bag
The current order information for needing to be arranged work order task is contained.
Process time is determined by the machine models or warehouse compartment type and the work order product attribute of work order task processed, is processed
Time is the Given information of scheduling.
The calculating of quantum object function needs to consider the pact of the production and manufacturing environment of heavy mechanical equipment in the step (2)
Beam condition.
The constraints is specially：During selection machine determines work order process time, big machinery is considered
Priority logical relation of the equipment work order task in work order process, work order task is once can not interrupt, i.e., single work
Single task can not in two times be processed across the unavailable time of machine, and two warehouse compartments can not be also assigned in a process gap and are kept in,
The single machine same time can only process a work order task, and machine only processes workpiece within the available period.
The constraints is expressed as follows by formula：
In formula (9), aijkRepresent work order i j-th of process whether the state being processed in k-th of machine；bijlTable
Show work order i j-th of process whether the state being processed in l processing sequences, machining state takes 0 or takes 1；
Referred to as sequential operator, represents serial number of each element in array,
bijThe institute of work order task processing sequence for depositing i-th of work order, j-th of process is stateful,For array bij
Transposition,Expression takes bijMiddle state is the serial number corresponding to 1 element,Dimension and bijIt is identical；
bi(j+1)The institute of work order task processing sequence for depositing+1 process of i-th of work order jth is stateful,For
Array bi(j+1)Transposition,Expression takes bi(j+1)Middle state is the serial number corresponding to 1 element,'s
Dimension and bi(j+1)It is identical.
First constraint aijk, bijl=0 or 1, all i, j, k and l is set up, it is ensured that chromosome coding it is feasible
Property；
Second constraintIt ensure that work order task order is appointed according to work order
The logical order of business processing is processed；
3rd constraintAll work order task orders are set up, it is ensured that all work orders
Task is only processed once；
4th constraintAll i and j are set up, it is ensured that single work order task is only in a machine
It is processed on device.
The binary system that the Fitness analysis of the step (2) need to be generated object function by quantum chromosomes population Q (t) is controlled
Variable X,
Obtained by formula (7) with formula (10),
In formula (11)A binary bit in variable is controlled for binary system,Value：
Wherein γ is random number, takes the arbitrary value in [0,1].
Variable X is controlled according to the binary system of generation, the order of work order task is corresponded to, scheduling is carried out, according to scheduling order
The deadline for calculating whole heavy mechanical equipment work order is the fitness value of quantum.
Step (2)：Quantum scheduling and Fitness analysis；
As shown in Fig. 2 scheduling step is as follows in the step (2)：
Step (201)：Quantum is decoded：Incoming single quantum is resolved to the scheduling order of work order task；
Step (202)：Scheduling queue L0 is treated in foundation, and the order deposit of decoded workman's task scheduling is treated into scheduling queue L0
In；
Step (203)：Judgement treats whether scheduling queue L0 is empty, if so, then scheduling is finished, calculates fitness value, terminates；
If it is not, the head work order task for treating scheduling queue L0 is then taken, into step (204)；
Step (204)：Whether judge current work order task is assembling work order；If being put into step (205)；Just enter if not
Enter step (206)；
Step (205)：Judge whether workpiece needed for specifying warehouse compartment is complete, if being put into step (206)；It is put into if not
Step (207)；
Step (206)：Whether judge possible schedule fragment is empty；Determine that if not it is available and its and start process time, more
New possible schedule fragment set；Foundation is had been lined up arranging L1, and current drained work order task is put into L1 afterbody, return to step
(203)；If so, being put into step (208)；
Step (207)：Judgement treats whether scheduling queue L0 is empty, if so, then scheduling fails, fitness value is set to positive nothing
Thoroughly, terminate；Just take if not and treat that scheduling queue L0 head work order task, for current work order task, then appoints the work order just handled
Business is put into the head position for treating scheduling queue L0, return to step (204)；
Step (208)：Whether be empty, if failing with regard to scheduling, fitness value is set to just infinite, terminates if judging L1；If not
The work order task just handled is just put into L0 head positions, it is current work order task to take L1 afterbody work orders task, when updating feasible
Between section set, return to step (206).
Treatment on special problems：If quantum group is not all arranged in the calculating in a whole generation, it may proceed to initialize a generation
Quantum continues optimizing, until reaching termination condition, stops this chromosome population iteration.Scheduling strategy has two classes, and a class is about
Shu Youxian a, class is that result is preferential.Under constraint-prioritized strategy, if not discharging result, pot life is returned to not enough, scheduling is lost
Lose.Under the preferential strategy of result, can according to order priority order, will be set to successively by the time it is just infinite, at this
Chromosome population optimizing is restarted under part, until finding feasible scheduling strategy.
Step (3)：Compare fitness value, fitness value reckling is correspondingFor optimal solution, optimal solution is preserved to B (t)；
Judge whether to reach termination condition, optimal solution is exported if termination condition is reached；Step is jumped to if not up to termination condition
(4)；
The B (t) is used to preserve population optimal solution, including locally optimal solution.
When step (3) termination condition is the maximum quantum update times set and/or reaches scheduling set in advance
Between desired threshold.
Step (4)：Quantum updates, and step is as follows：
Step (4-1)：T=t+1 is made, X is generated by Q (t-1), according to X binary sequence amount of calculation specific item scalar functions；
Step (4-2)：With Quantum rotating gate Population Regeneration Q (t).
The Quantum rotating gate selects similar Givens orthogonal matrixes form, and formula is as follows：
In formulaFor the Quantum rotating gate of i-th of quantum bit position of j-th of chromosome that evolutionary generation is t, For revolving door parameter,For the anglec of rotation, anglec of rotation angular dimension takes fixed value
0.02 π, anglec of rotation direction of rotation is determined according to table 1.
The anglec of rotation inquiry table of table 1
In table 1A binary bit in variable is controlled for binary system,To be stored in the optimal solution in B (t)
The binary bit of correspondence position.
The Quantum rotating gate more new formula is：
Quantum bit after renewal needs to meet,
Step (4-3)：Return to step (2) calculates the fitness value of quantum after renewal, by the X corresponding to minimum fitness value
Preserve to B (t).
Although above-mentioned the embodiment of the present invention is described with reference to accompanying drawing, not to present invention protection model
The limitation enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not
Need to pay various modifications or deform still within protection scope of the present invention that creative work can make.
Claims (10)
1. the complex scene scheduling method based on quantum evolutionary algorithm, it is characterized in that, comprise the following steps：
Step (1)：Quantum population is initialized：Encoded and first including initialization quantum population evolutionary generation, initialization quantum bit
Beginningization quantum chromosomes quantity；
Step (2)：Quantum scheduling and Fitness analysis：Set up quantum object function, it is considered to the manufacturing of heavy mechanical equipment
Under the constraints of environment, quantum object function is solved；The binary system control variable of quantum population generation object function,
Variable is controlled according to the binary system of generation, the order of work order task is corresponded to, scheduling is carried out, calculates whole big according to scheduling order
The fitness value of the deadline, as quantum of type plant equipment work order；
Step (3)：Compare fitness value, optimal solution is determined by minimum fitness value；Preserve optimal solution；Judge whether to reach knot
Beam condition, exports optimal solution if termination condition is reached；Step (4) is jumped to if not up to termination condition；
Step (4)：Quantum updates：Quantum population is updated using Quantum rotating gate；Return to step (2) recalculates quantum
Fitness value, continue optimizing.
2. the complex scene scheduling method as claimed in claim 1 based on quantum evolutionary algorithm, it is characterized in that,
Quantum population is initialized in the step (1), is comprised the following steps：
Step (1-1)：Initialize evolutionary generation t=0；
Step (1-2)：Initialize quantum bit coding：By determine the selected machine of heavy mechanical equipment work order task and
Reach the standard grade the order of processing, to obtain more excellent quantum object function；
Need to define two variables in initialization procedure：
First variable represents whether work order i j-th of process processes on k-th of machine, is designated as variable aijkIf, selection
Machine k then variable aijkValue is 1, the variable a if non-selected machine kijkValue is 0；
Second variable represents that the processing sequence of work order i j-th of process is l, is designated as variable bijl, when work order i j-th of work
Sequence start to reach the standard grade at l-th of processing sequence processing when, variable bijl1 is designated as, otherwise variable bijlFor 0；
Make A=[a111,a112..., aijk...], whether processed for depositing work order i j-th of process on k-th of machine
State；
Make B=[b111,b112..., bijl...], whether j-th of process for depositing work order i is opened at l-th of processing sequence
Begin the state of processing of reaching the standard grade；
Sequential concatenation is carried out to A and B, the quantum bit coding of multiple heavy mechanical equipment work orders is obtained, is designated as X；
X=A+B=[a111,a112..., aijk,…,b111,b112..., bijl,…] (1)
Wherein, the "+" order of representation splicing in formula (1), aijk、bijl0 is taken respectively or takes 1, X to be binary sequence；
Work order task, process equipment and manufacturing procedure information are contained in binary sequence X by the coded system；
Specifically, a quantum bit position is expressed as：The state representation of quantum bit is：|ψ>=α | 0 ＞+β | 1>,
Wherein | 0 ＞ and | 1 ＞ represents two kinds of basic status of quantum bit；α and β is plural number, represents the probability amplitude of qubit state,
α2Probability, the β of state 0 are in for quantum bit2For probability of the quantum bit in state 1, α and β amplitude and angle random take
Value, but meet | α |2+|β|2=1；
Q is made to represent chromosome, then the quantum chromosomes that m quantum bit position is represented are
<mrow>
<mi>q</mi>
<mo>=</mo>
<mo>&lsqb;</mo>
<mrow>
<mtable>
<mtr>
<mtd>
<msub>
<mi>&alpha;</mi>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>&alpha;</mi>
<mn>2</mn>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>&beta;</mi>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>&beta;</mi>
<mn>2</mn>
</msub>
</mtd>
</mtr>
</mtable>
<mn>...</mn>
<mtable>
<mtr>
<mtd>
<msub>
<mi>&alpha;</mi>
<mi>m</mi>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>&beta;</mi>
<mi>m</mi>
</msub>
</mtd>
</mtr>
</mtable>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, αi 2+βi 2=1, i=1,2 ..., m；M represents individual chromosome quantum bit bit length, and m calculation formula is as follows：
M=work orders total task number × number of devices+work order total task number2 (3)
Then formula (1) is expressed as with quantum bit：
<mrow>
<mi>X</mi>
<mo>=</mo>
<mo>&lsqb;</mo>
<mrow>
<mtable>
<mtr>
<mtd>
<msub>
<mi>&alpha;</mi>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>&alpha;</mi>
<mn>2</mn>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>&beta;</mi>
<mn>1</mn>
</msub>
</mtd>
<mtd>
<msub>
<mi>&beta;</mi>
<mn>2</mn>
</msub>
</mtd>
</mtr>
</mtable>
<mn>...</mn>
<mtable>
<mtr>
<mtd>
<msub>
<mi>&alpha;</mi>
<mi>m</mi>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>&beta;</mi>
<mi>m</mi>
</msub>
</mtd>
</mtr>
</mtable>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
Step (1-3)：Initialize population：Chromosome population is associated with evolutionary generation t, if the quantum chromosomes population in t generations is：
<mrow>
<mi>Q</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>&lsqb;</mo>
<msubsup>
<mi>q</mi>
<mn>1</mn>
<mi>t</mi>
</msubsup>
<mo>,</mo>
<msubsup>
<mi>q</mi>
<mn>2</mn>
<mi>t</mi>
</msubsup>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msubsup>
<mi>q</mi>
<mi>j</mi>
<mi>t</mi>
</msubsup>
<mo>,</mo>
<mn>...</mn>
<msubsup>
<mi>q</mi>
<mi>n</mi>
<mi>t</mi>
</msubsup>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
Then formula (2) is converted into：
<mrow>
<msubsup>
<mi>q</mi>
<mi>j</mi>
<mi>t</mi>
</msubsup>
<mo>=</mo>
<mo>&lsqb;</mo>
<mrow>
<mtable>
<mtr>
<mtd>
<msubsup>
<mi>&alpha;</mi>
<mrow>
<mi>j</mi>
<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
<mtd>
<msubsup>
<mi>&alpha;</mi>
<mrow>
<mi>j</mi>
<mn>2</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
<mtr>
<mtd>
<msubsup>
<mi>&beta;</mi>
<mrow>
<mi>j</mi>
<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
<mtd>
<msubsup>
<mi>&beta;</mi>
<mrow>
<mi>j</mi>
<mn>2</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
</mtable>
<mn>...</mn>
<mtable>
<mtr>
<mtd>
<msubsup>
<mi>&alpha;</mi>
<mrow>
<mi>j</mi>
<mi>m</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
<mtr>
<mtd>
<msubsup>
<mi>&beta;</mi>
<mrow>
<mi>j</mi>
<mi>m</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
</mtable>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, j represents chromosome number, j=1,2 ... n, and n is that chromosome population scale is total chromosome number amount；
Then formula (4) is converted into after being associated with evolutionary generation t：
<mrow>
<msubsup>
<mi>X</mi>
<mi>j</mi>
<mi>t</mi>
</msubsup>
<mo>=</mo>
<mo>&lsqb;</mo>
<mrow>
<mtable>
<mtr>
<mtd>
<msubsup>
<mi>&alpha;</mi>
<mrow>
<mi>j</mi>
<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
<mtd>
<msubsup>
<mi>&alpha;</mi>
<mrow>
<mi>j</mi>
<mn>2</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
<mtr>
<mtd>
<msubsup>
<mi>&beta;</mi>
<mrow>
<mi>j</mi>
<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
<mtd>
<msubsup>
<mi>&beta;</mi>
<mrow>
<mi>j</mi>
<mn>2</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
</mtable>
<mn>...</mn>
<mtable>
<mtr>
<mtd>
<msubsup>
<mi>&alpha;</mi>
<mrow>
<mi>j</mi>
<mi>m</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
<mtr>
<mtd>
<msubsup>
<mi>&beta;</mi>
<mrow>
<mi>j</mi>
<mi>m</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
</mtable>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
<mo>.</mo>
</mrow>
3. the complex scene scheduling method as claimed in claim 2 based on quantum evolutionary algorithm, it is characterized in that,
Quantum fitness is quantum object function in the step (2), and the scheduling strategy used under limited space scene is just
Row's strategy, expects that the order of processing is delivered goods as early as possible；
Quantum object function：
Minf (X)=Min ∑sAll work orders(heavy mechanical equipment work order planned end time-heavy mechanical equipment work order plan starts
Time) (8)
X is referred to as the binary system control variable of object function, is one group of binary sequence, the quantum target function value is smaller more
It is good；
Quantum fitness calculate be by according to individual chromosome value obtain work order task each machine reach the standard grade process time and
Downtime, also referred to as the work order task order according to given by chromosome carry out the process of scheduling；
Numerical value one work order task of correspondence in individual chromosome in chromosome population per dimension, each chromosome is contained
Currently need to be arranged the order information of work order task；
Process time determines by the machine models or warehouse compartment type and the work order product attribute of work order task processed, process time
For the Given information of scheduling.
4. the complex scene scheduling method as claimed in claim 3 based on quantum evolutionary algorithm, it is characterized in that,
The calculating of quantum object function needs to consider the constraint bar of the production and manufacturing environment of heavy mechanical equipment in the step (2)
Part；
The constraints is specially：During selection machine determines work order process time, heavy mechanical equipment is considered
Priority logical relation of the work order task in work order process, work order task is once that can not interrupt, i.e., single work order is appointed
Business can not in two times be processed across the unavailable time of machine, and two warehouse compartments can not be also assigned in a process gap and are kept in, separate unit
The machine same time can only process a work order task, and machine only processes workpiece within the available period；
The constraints is expressed as follows by formula：
In formula (9), aijkRepresent work order i j-th of process whether the state being processed in k-th of machine；bijlRepresent work
Single i j-th of process whether the state being processed in l processing sequences, machining state takes 0 or takes 1；
Referred to as sequential operator, represents serial number of each element in array,
bijThe institute of work order task processing sequence for depositing i-th of work order, j-th of process is stateful,For array bijTurn
Put,Expression takes bijMiddle state is the serial number corresponding to 1 element,Dimension and bijIt is identical；
bi(j+1)The institute of work order task processing sequence for depositing i-th of individual process of work order (j+1) is stateful,For number
Group bi(j+1)Transposition,Expression takes bi(j+1)Middle state is the serial number corresponding to 1 element,Dimension
Number and bi(j+1)It is identical；
First constraint aijk, bijl=0 or 1, all i, j, k and l are set up, it is ensured that the feasibility of chromosome coding；
Second constraintIt ensure that work order task order is processed according to work order task
Logical order be processed；
3rd constraintAll work order task orders are set up, it is ensured that all work order tasks
Only it is processed once；
4th constraintAll i and j are set up, it is ensured that single work order task is only on a machine
It is processed.
5. the complex scene scheduling method as claimed in claim 4 based on quantum evolutionary algorithm, it is characterized in that,
The Fitness analysis of the step (2) controls variable X by quantum chromosomes population Q (t) binary systems for generating object function,
<mrow>
<mi>X</mi>
<mo>=</mo>
<msubsup>
<mi>X</mi>
<mi>j</mi>
<mi>t</mi>
</msubsup>
<mo>=</mo>
<mo>&lsqb;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mn>2</mn>
</mrow>
<mi>t</mi>
</msubsup>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mi>m</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
Obtained by formula (7) with formula (10),
<mrow>
<mi>X</mi>
<mo>=</mo>
<msubsup>
<mi>X</mi>
<mi>j</mi>
<mi>t</mi>
</msubsup>
<mo>=</mo>
<mo>&lsqb;</mo>
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mn>2</mn>
</mrow>
<mi>t</mi>
</msubsup>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mi>m</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mrow>
<mo>&rsqb;</mo>
<mo>=</mo>
<mo>&lsqb;</mo>
<mrow>
<mtable>
<mtr>
<mtd>
<msubsup>
<mi>&alpha;</mi>
<mrow>
<mi>j</mi>
<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
<mtd>
<msubsup>
<mi>&alpha;</mi>
<mrow>
<mi>j</mi>
<mn>2</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
<mtr>
<mtd>
<msubsup>
<mi>&beta;</mi>
<mrow>
<mi>j</mi>
<mn>1</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
<mtd>
<msubsup>
<mi>&beta;</mi>
<mrow>
<mi>j</mi>
<mn>2</mn>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
</mtable>
<mn>...</mn>
<mtable>
<mtr>
<mtd>
<msubsup>
<mi>&alpha;</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
<mtr>
<mtd>
<msubsup>
<mi>&beta;</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
</mtable>
<mn>...</mn>
<mtable>
<mtr>
<mtd>
<msubsup>
<mi>&alpha;</mi>
<mrow>
<mi>j</mi>
<mi>m</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
<mtr>
<mtd>
<msubsup>
<mi>&beta;</mi>
<mrow>
<mi>j</mi>
<mi>m</mi>
</mrow>
<mi>t</mi>
</msubsup>
</mtd>
</mtr>
</mtable>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (11)A binary bit in variable is controlled for binary system,Value：
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
</mtd>
<mtd>
<mrow>
<mi>&gamma;</mi>
<mo>&Element;</mo>
<mo>&lsqb;</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>&rsqb;</mo>
<mo>></mo>
<mo>|</mo>
<msubsup>
<mi>&alpha;</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mi>t</mi>
</msubsup>
<msup>
<mo>|</mo>
<mn>2</mn>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<mi>o</mi>
<mi>t</mi>
<mi>h</mi>
<mi>e</mi>
<mi>r</mi>
<mi>w</mi>
<mi>i</mi>
<mi>s</mi>
<mi>e</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>12</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein γ is random number, takes the arbitrary value in [0,1]；
Variable X is controlled according to the binary system of generation, the order of work order task is corresponded to, scheduling is carried out, calculated according to scheduling order
The deadline of whole heavy mechanical equipment work order is the fitness value of quantum.
6. the complex scene scheduling method as claimed in claim 5 based on quantum evolutionary algorithm, it is characterized in that,
The scheduling principle：Scheduling comprising inventory limitation scene uses the plug hole scheduling method with time window；In scheduling process
In, a dimension will be regarded the time as, regard all occupied machine resources, each warehouse compartment and conveying arrangement as container, often
Platform machine, warehouse compartment and conveying arrangement are with an available time window in finite time；According to the actual conditions of production line,
Increase the storage process in line side storehouse between two-step, regard available machines used as line side Kuku position, line side storehouse storage process institute's used time
Between be set to an insignificant time interval, while increasing corresponding work order task according to the difference of work order；
The time window that original production can be put in storage according to the machine of producing line start shooting or process calendar initialized, warehouse compartment also according to
Pot life initialization time window, time window comprising one or more pot lifes section, each pot life section inner machine or
Warehouse compartment is used for processing or depositing a workpiece.
7. the complex scene scheduling method as claimed in claim 6 based on quantum evolutionary algorithm, it is characterized in that,
Scheduling step is as follows in the step (2)：
Step (201)：Quantum is decoded：Incoming single quantum is resolved to the scheduling order of work order task；
Step (202)：Scheduling queue L0 is treated in foundation, and the order deposit of decoded workman's task scheduling is treated in scheduling queue L0；
Step (203)：Judgement treats whether scheduling queue L0 is empty, if so, then scheduling is finished, calculates fitness value, terminates；If
It is no, then the head work order task for treating scheduling queue L0 is taken, into step (204)；
Step (204)：Whether judge current work order task is assembling work order；If being put into step (205)；Step is put into if not
Suddenly (206)；
Step (205)：Judge whether workpiece needed for specifying warehouse compartment is complete, if being put into step (206)；Step is put into if not
(207)；
Step (206)：Whether judge possible schedule fragment is empty；Determine that if not it is available and its and start process time, renewal can
The row period gathers；Foundation is had been lined up arranging L1, and current drained work order task is put into L1 afterbody, return to step (203)；If
It is to be put into step (208)；
Step (207)：Judgement treats whether scheduling queue L0 is empty, if so, then scheduling fails, fitness value is set to just infinite, ties
Beam；Just take if not and treat that scheduling queue L0 head work order task, for current work order task, then puts the work order task just handled
Enter to treat scheduling queue L0 head position, return to step (204)；
Step (208)：Whether be empty, if failing with regard to scheduling, fitness value is set to just infinite, terminates if judging L1；Just will if not
The work order task just handled is put into L0 head positions, and it is current work order task to take L1 afterbody work orders task, updates possible schedule fragment
Set, return to step (206).
8. the complex scene scheduling method as claimed in claim 1 based on quantum evolutionary algorithm, it is characterized in that,
The step (3) compares fitness value, and fitness value reckling is correspondingFor optimal solution, optimal solution is preserved to B (t)；
Judge whether to reach termination condition, optimal solution is exported if termination condition is reached；Step is jumped to if not up to termination condition
(4)；
The B (t) is used to preserve population optimal solution, including locally optimal solution；
Step (3) termination condition is the maximum quantum update times set and/or reaches scheduling time reason set in advance
Think threshold value.
9. the complex scene scheduling method as claimed in claim 1 based on quantum evolutionary algorithm, it is characterized in that,
Step (4) quantum updates, and comprises the following steps：
Step (4-1)：T=t+1 is made, X is generated by Q (t-1), according to X binary sequence amount of calculation specific item scalar functions；
Step (4-2)：With Quantum rotating gate Population Regeneration Q (t)；
The Quantum rotating gate selects similar Givens orthogonal matrixes form, formula：
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In formulaFor the Quantum rotating gate of i-th of quantum bit position of j-th of chromosome that evolutionary generation is t, It is revolving door parameter,For the anglec of rotation, anglec of rotation angular dimension takes fixation
The π of value 0.02,
The Quantum rotating gate more new formula is：
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Quantum bit after renewal needs to meet,
Step (4-3)：Return to step (2) calculates the fitness value of quantum after renewal, and the X corresponding to minimum fitness value is preserved
To B (t).
10. the complex scene product plan based on quantum evolutionary algorithm, it is characterized in that, including：Memory, processor and storage
The computer program run on a memory and on a processor, realization is following during computer program described in the computing device
Step：
Step (1)：Quantum population is initialized：Encoded and first including initialization quantum population evolutionary generation, initialization quantum bit
Beginningization quantum chromosomes quantity；
Step (2)：Quantum scheduling and Fitness analysis：Set up quantum object function, it is considered to the manufacturing of heavy mechanical equipment
Under the constraints of environment, quantum object function is solved；The binary system control variable of quantum population generation object function,
Variable is controlled according to the binary system of generation, the order of work order task is corresponded to, scheduling is carried out, calculates whole big according to scheduling order
The fitness value of the deadline, as quantum of type plant equipment work order；
Step (3)：Compare fitness value, optimal solution is determined by minimum fitness value；Preserve optimal solution；Judge whether to reach knot
Beam condition, exports optimal solution if termination condition is reached；Step (4) is jumped to if not up to termination condition；
Step (4)：Quantum updates：Quantum population is updated using Quantum rotating gate；Return to step (2) recalculates quantum
Fitness value, continue optimizing.
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