CN110188951A - A kind of method for building up of the optimizing scheduling of the brick field ferry bus based on least energy consumption - Google Patents
A kind of method for building up of the optimizing scheduling of the brick field ferry bus based on least energy consumption Download PDFInfo
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Abstract
The method for building up of the optimizing scheduling of the present invention relates to a kind of brick field ferry bus based on least energy consumption, comprising the following steps: 1) establish the topology diagram of brick field movement system, be based on site layout project map analysis ferry bus operating condition；2) double ferry bus operations and task assignment constraints condition are established for the scheduling of double ferry buses of same track according to actual condition；3) with the minimum optimization object function of energy consumption, the task execution sequence of ferry bus is planned；4) mutation probability p in genetic algorithm is analyzedmWith crossover probability pcCalculation formula；5) according to mutation probability pmWith crossover probability pcCalculating, establish improved adaptive pmAnd pcGenetic algorithm.The present invention provides a kind of methods of the double ferry bus optimizing schedulings of brick field with the minimum target of energy consumption, and a kind of improved adaptive GA-IAGA that can solve the problems, such as the MIXED INTEGER nonlinear mathematics programming is established, the realization of entire brick and tile product production line automation and the raising of efficiency are conducive to.
Description
Technical field
The present invention relates to intelligent algorithm field, in particular to the optimizing scheduling of a kind of brick field ferry bus based on least energy consumption
Method.
Background technique
With the rapid development of social economy, a large amount of infrastructure is like a raging fire, manufacturing industry once backward in technique
It gradually marches toward automation, Informatization Development.Currently, domestic many brick and tile product production lines have had been introduced by Automatic coal blending, have matched
Water, the technologies such as automatic cloth base stacking, the control of tunnel oven temperature, can greatly improve production efficiency.But brick and tile product production line
Full-automatic realize of transhipment control be but still in compared with low water-mark, Some Enterprises also using manual operation, cause production efficiency
Lowly.Drying, roasting for needing a plurality of parallel track to realize adobe in brick and tile production line etc. and the transhipment of kiln car need logical
Ferry bus is crossed to realize, thus to kiln car transhipment when process optimization and the scheduling of ferry bus is just particularly important, one
Denier runs the unsmooth efficiency that will greatly influence entire movement system.
Summary of the invention
In view of this, the optimizing scheduling of the purpose of the present invention is to provide a kind of brick field ferry bus based on least energy consumption is calculated
Method, for optimizing the scheduling problem of ferry bus in brick and tile product movement system.
In a first aspect, a kind of optimizing scheduling algorithm of brick field ferry bus based on least energy consumption provided by the invention, is logical
Cross following technical scheme realization:
Method the following steps are included:
Step S1: establishing the topology diagram of brick field movement system, is based on site layout project map analysis ferry bus operating condition,
By the analysis to the brick and tile product technological process of production, the topological structure of brick field movement system is obtained；
Step S2: it according to actual condition, for the scheduling of double ferry buses of same track, establishes model and determines that double pendulum crosses
Vehicle operation and task assignment constraints condition；
Step S3: with the minimum optimization object function of energy consumption, the task execution sequence of ferry bus is planned；
Step S4: the improved adaptive GA-IAGA based on parallel machine scheduling carries out the solution of double ferry bus optimizing schedulings, and analysis is lost
Mutation probability p in propagation algorithmmWith crossover probability pcCalculation formula；
Step S5: according to mutation probability pmWith crossover probability pcCalculating, establish improved adaptive pmAnd pcHeredity is calculated
Method.
Particularly, in the step S2, the scheduling problem progress model foundation for double ferry buses includes following sub-step:
Step S21: parametric assumption:
N: the number of tasks that parallel machine need to execute, wherein i={ 1,2 ..., N }；
K: parallel machine quantity；
tik: K platform machine the time it takes when executing task i；
Cik: the time of K platform machine i after having executed task；
Sik: time of the K platform machine when starting execution task i；
Zi: after task i is performed, wait pending set of tasks.Wherein Z0={ 1,2 ..., N }；
Nk: the number of tasks needed to be implemented on K platform machine；
Pkt: K platform machine is in the position of t moment；
Ti: the regulation deadline of task i；
ωi: the weight of task i, when task i fails in stipulated time TiIt is inside executed, then the consumption value of its energy need to multiply
Upper weighted value ωi；
Eik: K platform machine has executed the energy that task i need to be consumed；
EIk: K platform machine is in unloaded kiln car, consumed energy in the idle running unit time；
Step S22: variable-definition is carried out:
Step S23: constraint condition is determined:
Cik=Sik+tikI=1,2 ..., N；K=1,2 ..., K (1)
Cik≤SrkI=1,2 ..., N；r∈Zi (2)
P2t-P1t≥L (4)
Formula (1) indicates that in task implementation procedure, the end time of current task is equal to current task Starting Executing Time
In addition execution task itself needs the time spent；
Formula (2) indicates that current machine currently could start next task after execution task, i.e. current machine is current
The task execution end time should be not more than the job start time of the next pending task of current machine；
Formula (3) indicates that for all machines, their total execution number of tasks is N；
Formula (4) indicates that two ferry buses need certain safe distance, avoids car to car impact with this, wherein L is indicated
The vehicle commander of ferry bus.
Particularly, in the step S3, optimization object function takes following steps to establish:
Step S31: energy consumption E when ferry bus zero load is determinedI, i.e., ferry bus is after having executed current task under
The consumption of energy before one task starts:
Step S32: energy consumption E when ferry bus full-load run is determinedB, i.e. energy of the ferry bus in task implementation procedure
Amount consumption, at the same need to study because the reason of collision avoidance and consumed energy will increase punishment formula when failing to complete task in time
Energy consumption:
ωi=a* (tik-Ti) (7)
Step S33: during the task execution of ferry bus, the wastage in bulk or weight of energy is equal to EBAnd EISum, it may be assumed that
E=EI+EB (8)
Step S34: it establishes with the objective function of the minimum optimization aim of total energy consumption:
Obj=min (E) (9)
The foundation of the objective function is that the task execution sequence of ferry bus is planned under the constraint of formula (1)~(4), so that pendulum
It is minimum to cross vehicle total energy consumption.
Particularly, in the step S4, the p of self-adapted genetic algorithmcAnd pmCalculation formula are as follows:
Wherein, fmaxFor individual in population maximum adaptation angle value；favgFor group's fitness average value；F ' is two wait intersect
Larger fitness value in individual；F is the fitness value to variation individual；Pc1And Pm1For the crossover probability in basic genetic algorithmic
And mutation probability.
Particularly, in the step S5, the solution of double ferry bus optimizing schedulings is carried out using Revised genetic algorithum, is improved
Genetic algorithm process it is as follows:
Step S51: coding mode uses real number expression way, and the mission number needed to be implemented is encoded to integer variable,
Each arrangement represents a kind of task execution scheme, and each position in arrangement has just respectively represented the task of each band number；
Integer coding is used for parallel machine scheduling problem and for one-dimension array, form is as follows:
A=[a1, a2..., an] (12)
ai=z*100+j i, j ∈ { 1,2 ..., n }, z ∈ { 1,2 ..., k } (13)
Wherein, n refers to the number of tasks to be executed, and k indicates parallel number of machines, and z refers to the code name of each machine, to guarantee
Execution task it is not repeated, there is not identical value in rear x numerical value of each element in numerical value a, can according to the numerical value of element
To know that task j is completed by machine z as, thus one-dimension array can be write to the form of matrix, be k row n column matrix:
Step S52: it carries out the setting of initial population: generating n integer according to formula (13), form random chromosomal, complete
Initialization of population；
Step S53: establish fitness function: this is each that is, in population using the inverse of objective function as fitness function
The fitness of individual:
F (k)=1/cmax(k) (15)
Wherein, cmax(k) the maximum energy consumption value of a scheduling representated by k-th of chromosome is indicated.
Step S54: carrying out the selection of population at individual, if the individual amount of initial population is n, individual fitness in population
Functional value is f (k), then the select probability of each individual are as follows:
Step S55: cross method, the random number for generating one (0,1), if the number is less than crossover probability p are establishedc, then right
Population selects improved crossover probability pcCrossover operation is carried out, which realizes pair principle two-by-two to all individuals；
Step S56: some genic value in some individual, then the random number for generating one (0,1) are chosen at random, if should
Number is less than mutation probability pm, then an integer is generated at random in section [1, k] to replace the first place of the genic value, use is adaptive
Answer the mutation probability p in genetic algorithmmTo carry out mutation operation；
Step S57: stop criterion sets a maximum number of iterations value, which is equal to greatest iteration
Stop when number.
Second aspect, a kind of electronic equipment are achieved through the following technical solutions, comprising: processor, memory and total
Line, wherein
The processor and the memory complete mutual communication by the bus；
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program to refer to
Order is able to carry out foregoing method.
A kind of third aspect, non-transient computer readable storage medium, is achieved through the following technical solutions: described non-
Transitory computer readable storage medium stores computer instruction, and it is foregoing that the computer instruction executes the computer
Method.
The beneficial effects of the present invention are:
The present invention is also in showing compared with low water-mark for full-automatic realize of transhipment control of current brick and tile product production line
Shape establishes model and determines double ferry bus operations and task according to actual condition for the scheduling of double ferry buses of same track
Assignment constraints condition plans the task execution sequence of ferry bus and based on parallel machine tune with the minimum optimization object function of energy consumption
The improved adaptive GA-IAGA of degree carries out the solution of double ferry bus optimizing schedulings, thus realize process optimization of the kiln car in transhipment and
Scheduling to ferry bus greatly improves the efficiency of entire movement system, realizes the full-automatic operation of transhipment control, adds
Speed brick and tile manufacture the automated process of industries, are conducive to the efficient production of brick and tile product production line and realize product class
Type diversification, the quality for improving brick and tile product and yield produce that quality is outstanding, brick and tile system of great variety of goods to realize science
Product.The present invention has high efficiency, energy saving and feasibility, accomplishes process reasonably optimizing, raw for building new automaticization brick field
Producing line is provided beneficial to thinking.
Other advantages, target and feature of the invention will be illustrated in the following description to a certain extent, and
And to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, Huo Zheke
To be instructed from the practice of the present invention.Target and other advantages of the invention can be wanted by following specification and right
Book is sought to be achieved and obtained.
Detailed description of the invention
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing to the present invention make into
The detailed description of one step, in which:
Fig. 1 is the movement system site layout project schematic diagram in the embodiment of the present invention；
Fig. 2 is the field layout figure of 2#, 3# ferry bus in embodiment；
Fig. 3 is the flow chart of self-adapted genetic algorithm；
Fig. 4 is general flow chart.
Specific embodiment
Hereinafter reference will be made to the drawings, and a preferred embodiment of the present invention will be described in detail.It should be appreciated that preferred embodiment
Only for illustrating the present invention, rather than limiting the scope of protection of the present invention.
In order to achieve the above objectives, the invention provides the following technical scheme:
As shown in figure 4, a kind of optimizing scheduling algorithm of brick field ferry bus based on least energy consumption of the invention, specifically includes
Following steps:
Step S1: it by the analysis to the brick and tile product technological process of production, is run based on site layout project map analysis ferry bus
Situation obtains the topological structure of brick field movement system；
The main process of the production technology of brick and tile product is: ingredient, water distribution, molding, drying, firing.Brick and tile product is whole
There is corresponding equipment in a production process to complete different processes.Such as: in the crusher in Raw material processing stage, by powder
It is squeezed into the brick machine of mud item, by the setting machine in adobe stacking to kiln car, fires the tunnel oven etc. in stage.Kiln car is then in brick and tile system
The production process of product plays the role for reprinting adobe.And when adobe needs to carry out next process flow, need ferry bus
To complete.Ground creeping vehicle, the dragger of each ferry bus and push-and-pull kiln car constitute the movement system of kiln car traveling.
Movement system process of the invention starts from the empty kiln car in empty wagons lane, and empty kiln car is transported to a yard base vehicle by ferry bus
On road, adobe on cloth base platform is good on empty kiln car by setting machine code, then is transported to the kiln car that code base is completed by ferry bus and dries in the air
Natural wind is carried out on base lane to air-dry, and is had ground creeping vehicle that kiln car is pushed to travel in orbit on base lane of drying in the air, is ferried after the completion of the base that dries in the air
High temperature drying is carried out in the drying kiln that kiln car can be transported on drying lane by vehicle, after the completion of dry, ferry bus transports kiln car
It is fired in tunnel oven on to roasting lane, kiln car is transported to finished product lane by ferry bus again and is unloaded after the completion of firing
Brick, the kiln car for unsnatching brick are transported to empty wagons lane exit by ferry bus, start to prepare for workflow next time, work as kiln
When vehicle damages, kiln car should be sent in kiln car maintenance lane and be overhauled.Wherein SB2, SB3 represent No. 2, No. 3 ferry buses, traveling
In No. 2 ferry bus running rails, SB4, SB5 represent No. 4, No. 5 ferry buses, travel on No. 4 ferry bus running rail brick field transhipments
The site layout project figure of system is as shown in Figure 1.
Kiln car during transhipment, due to the self-operating direction of kiln car cannot occur turn phenomenon, so kiln car from
One process flow, which is transported to, to be needed to realize by ferry bus in next process flow.It has been directed to a ferry bus operation
The case where having two ferry buses on track while running, when more kiln cars of appearance need the case where being transported through, two ferry-boats
Vehicle need to work at the same time, and based on the research to scheduling problem of multicomputer, can consider the task execution of two ferry buses at parallel machine
Scheduling problem.Thus it sets up with the two-shipper scheduling model of the minimum optimization aim of ferry bus total energy consumption, and it is solved.
The optimization of solving result is realized the invention proposes being solved using genetic algorithm for the solution of model.
Step S2: it according to actual condition, for the scheduling of double ferry buses of same track, establishes model and determines that double pendulum crosses
Vehicle operation and task assignment constraints condition；
For the scheduling problem that two ferry buses are run on the same track, the scheduling problem and parallel machine tune of double ferry buses
Degree problem is similar, therefore can the needing to be implemented ferry bus of the task is considered as the workpiece for needing to process, and two ferry buses are considered as to work
The machine that part is processed, when occurring multiple tasks simultaneously and needing to be implemented, need two ferry buses carry out task distribution with
And each task executes sequence.
For movement system process, 2#, 3# ferry bus and 4#, 5# ferry bus are transported on same track respectively
Row, ferry bus execute task be all driven to from initial position task initial position draw kiln car after drive to target position again, and
After in the kiln car push-and-pull to target lane of transhipment, ferry bus rests in the target position for completing this task, this subtask is completed.
Since 2#, 3# ferry bus are similar with 4#, 5# ferry bus Problems of Optimal Dispatch, this patent is research pair with 2#, 3# ferry bus
As being analyzed, the field layout of two ferry buses is as shown in Figure 2.
By taking SB2, SB3 ferry bus as an example, respectively representing parallel machine 1 and parallel machine 2, i.e. parallel machine number is 2, and
They run on a track, so also needing to consider the collision problem of two vehicles, carry out for the scheduling problem of double ferry buses
Model foundation:
Step S21. parametric assumption:
N: the number of tasks that parallel machine need to execute, wherein i={ 1,2 ..., N }；
K: parallel machine quantity；
tik: K platform machine the time it takes when executing task i；
Cik: the time of K platform machine i after having executed task；
Sik: time of the K platform machine when starting execution task i；
Zi: after task i is performed, wait pending set of tasks.Wherein Z0={ 1,2 ..., N }；
Nk: the number of tasks needed to be implemented on K platform machine；
Pkt: K platform machine is in the position of t moment；
Ti: the regulation deadline of task i；
ωi: the weight of task i, when task i fails in stipulated time TiIt is inside executed, then the consumption value of its energy need to multiply
Upper weighted value ωi；
Eik: K platform machine has executed the energy that task i need to be consumed；
EIk: K platform machine is in unloaded kiln car, consumed energy in the idle running unit time；
Step S22: variable-definition is carried out:
Step S23: constraint condition is determined:
Cik=Sik+tikI=1,2 ..., N；K=1,2 ..., K (1)
Cik≤SrkI=1,2 ..., N；r∈Zi (2)
P2t-P1t≥L (4)
Formula (1) indicates that in task implementation procedure, the end time of current task is equal to current task Starting Executing Time
In addition execution task itself needs the time spent.
Formula (2) indicates that current machine currently could start next task after execution task, i.e. current machine is current
The task execution end time should be not more than the job start time of the next pending task of current machine.
Formula (3) indicates that for all machines, their total execution number of tasks is N.
Formula (4) indicates that two ferry buses need certain safe distance, avoids car to car impact with this, wherein L is indicated
The vehicle commander of ferry bus.
Step S3: with the minimum optimization object function of energy consumption, the task execution sequence of ferry bus is planned；
The present invention selects directly to have reacted energy consumption problem and the enterprise of ferry bus with the minimum optimization aim of energy consumption
The problem more paid close attention in production run.Energy consumption is saved from Problems of Optimal Dispatch, exactly fully considers brick and tile system
Under the premise of the series of factors such as the behavior pattern of the characteristics of product, equipment, reasonable allocation schedule task makes ferry bus energy consumption most
It is small.
Shown in the following two o'clock of the Energy Expenditure Levels of ferry bus:
1) energy consumption E when ferry bus zero loadI, i.e., ferry bus is after having executed current task until next task is opened
The consumption of energy before beginning:
2) energy consumption E when ferry bus full-load runB, i.e. energy consumption of the ferry bus in task implementation procedure, together
When need to study because consumed energy will increase the energy consumption of punishment formula when failing to complete task the reason of collision avoidance in time:
ωi=a* (tik-Ti) (7)
During the task execution of ferry bus, the wastage in bulk or weight of energy is equal to EBAnd EISum, it may be assumed that
E=EI+EB (8)
Therefore, it establishes with the objective function of the minimum optimization aim of total energy consumption:
Obj=min (E) (9)
The foundation of the objective function is that the task execution sequence of ferry bus is planned under the constraint of formula (1)~(4), so that pendulum
It is minimum to cross vehicle total energy consumption.
The mathematical model with the minimum optimization aim of energy consumption that the present invention establishes is a MIXED INTEGER nonlinear mathematics rule
The problem of drawing has the distinctive NP problem characteristic of scheduling problem, and constraint is more complicated, solves more complicated.Therefore number is used
It learns to calculate and be combined with intelligent algorithm, since genetic algorithm has been shown by force in the distinctive NP problem characteristic of solution scheduling problem
Big ability of searching optimum, therefore the present invention carries out the solution of double ferry bus optimizing schedulings using Revised genetic algorithum.
Step S4: the improved adaptive GA-IAGA based on parallel machine scheduling carries out the solution of double ferry bus optimizing schedulings, and analysis is lost
Mutation probability p in propagation algorithmmWith crossover probability pcCalculation formula；
In genetic algorithm, mutation probability pmWith crossover probability pcPerformance and convergence to algorithm etc. have biggish shadow
It rings, pcAnd pmValue it is most important.Enable pcAnd pmValue change automatically with the variation of fitness function value.Adaptively
The p of genetic algorithmcAnd pmCalculation formula are as follows:
Wherein, fmaxFor individual in population maximum adaptation angle value；favgFor group's fitness average value；F ' is two wait intersect
Larger fitness value in individual；F is the fitness value to variation individual；Pc1And Pm1For the crossover probability in basic genetic algorithmic
And mutation probability.According to above formula to pcAnd pmIt is calculated, can preferably guarantee convergence and global optimum
Solution is sought.
Step S5: according to mutation probability pmWith crossover probability pcCalculating, establish improved adaptive pmAnd pcHeredity is calculated
Method realizes the solution to double ferry bus optimizing schedulings.
In the present embodiment, as shown in figure 3, the process of improved adaptive GA-IAGA is as follows:
Step S51: coding mode uses real number expression way, and the mission number needed to be implemented is encoded to integer variable,
Each arrangement represents a kind of task execution scheme, and each position in arrangement has just respectively represented the task of each band number.
Integer coding is used for parallel machine scheduling problem and for one-dimension array, form is as follows:
A=[a1, a2..., an] (12)
ai=z*100+j i, j ∈ { 1,2 ..., n }, z ∈ { 1,2 ..., k } (13)
Wherein, n refers to the number of tasks to be executed, and k indicates parallel number of machines, and z refers to the code name of each machine.To guarantee
Execution task it is not repeated, there is not identical value in rear x numerical value of each element in numerical value a.It can according to the numerical value of element
To know that task j is completed by machine z as, thus one-dimension array can be write to the form of matrix, be k row n column matrix:
Above-mentioned matrix both shown the distribution condition of task and also uniform machinery of having withdrawn deposit on execute task sequencing
(columns where column determines), and matrix has following characteristic:
(1) each only one element of column is not 0 in matrix；
(2) numerical value not for 0 element only occurs once；
(3) each element numberical range is 1-n.
Step S52: the setting of initial population.After having carried out code Design, the setting of initial population need to be carried out.Because compiling
The code design phase ensures the randomness of the integer of generation, to generate n integer according to formula (13), forms random chromosomal,
Complete initialization of population.
Step S53: the establishment of fitness function.The objective function of this patent is the power consumption values of ferry bus, is one non-negative
Number, using the inverse of objective function as fitness function, i.e., the fitness of each individual in population:
Wherein, cmax(k) the maximum energy consumption value of a scheduling representated by k-th of chromosome is indicated.
Step S54: the selection of population at individual.This patent is using classical roulette and ratio selection mode come to population
Body is selected.According to formula (15) it is found that the power consumption values of objective function are bigger, then for fitness function value with regard to smaller, individual is selected
The probability selected is lower；The power consumption values of objective function are smaller, then fitness function value is bigger, and the probability that individual is selected is got over
It is high.
If the individual amount of initial population is n, individual fitness function value is f (k), the then choosing of each individual in population
Select probability are as follows:
Step S55: cross method is established.Partially matched crossover method is used at this, cross method is as shown in the table:
The citing of 1 partially matched crossover method of table
The random number for generating one (0,1), if the number is less than crossover probability pc, then crossover operation, the behaviour are carried out to population
All individuals of opposing realize pair principle two-by-two.
According in self-adapted genetic algorithm to the improvement of crossover probability, improved crossover probability pcValue can be according to suitable
The variation of response functional value and change automatically, the p in self-adapted genetic algorithmcIt can be mentioned according to the occurrence of each solution
For best pcValue.Therefore, improved crossover probability p is selectedcTo carry out crossover operation.
Step S56: mutation operation.Some genic value in some individual is chosen at random, then generates one (0,1) at random
Number, if the number be less than mutation probability pm, then an integer is generated at random in section [1, k] to replace the head of the genic value
Position, and as it appears from the above, using the mutation probability p in self-adapted genetic algorithmmTo carry out mutation operation.
Step S57: stop criterion sets a maximum number of iterations value, which is equal to greatest iteration
Stop when number.
It should be appreciated that the embodiment of the present invention can be by computer hardware, the combination of hardware and software or by depositing
The computer instruction in non-transitory computer-readable memory is stored up to be effected or carried out.Standard volume can be used in the method
Journey technology-includes that the non-transitory computer-readable storage media configured with computer program is realized in computer program,
In configured in this way storage medium computer is operated in a manner of specific and is predefined --- according in a particular embodiment
The method and attached drawing of description.Each program can with the programming language of level process or object-oriented come realize with department of computer science
System communication.However, if desired, the program can be realized with compilation or machine language.Under any circumstance, which can be volume
The language translated or explained.In addition, the program can be run on the specific integrated circuit of programming for this purpose.
In addition, the operation of process described herein can be performed in any suitable order, unless herein in addition instruction or
Otherwise significantly with contradicted by context.Process described herein (or modification and/or combination thereof) can be held being configured with
It executes, and is can be used as jointly on the one or more processors under the control of one or more computer systems of row instruction
The code (for example, executable instruction, one or more computer program or one or more application) of execution, by hardware or its group
It closes to realize.The computer program includes the multiple instruction that can be performed by one or more processors.
Further, the method can be realized in being operably coupled to suitable any kind of computing platform, wrap
Include but be not limited to PC, mini-computer, main frame, work station, network or distributed computing environment, individual or integrated
Computer platform or communicated with charged particle tool or other imaging devices etc..Each aspect of the present invention can be to deposit
The machine readable code on non-transitory storage medium or equipment is stored up to realize no matter be moveable or be integrated to calculating
Platform, such as hard disk, optical reading and/or write-in storage medium, RAM, ROM, so that it can be read by programmable calculator, when
Storage medium or equipment can be used for configuration and operation computer to execute process described herein when being read by computer.This
Outside, machine readable code, or part thereof can be transmitted by wired or wireless network.When such media include combining microprocessor
Or other data processors realize steps described above instruction or program when, invention as described herein including these and other not
The non-transitory computer-readable storage media of same type.When the website according to the present invention based on big data log analysis
When intrusion detection method and technology program, the invention also includes computers itself.
Computer program can be applied to input data to execute function as described herein, to convert input data with life
At storing to the output data of nonvolatile memory.Output information can also be applied to one or more output equipments as shown
Device.In the preferred embodiment of the invention, the data of conversion indicate physics and tangible object, including the object generated on display
Reason and the particular visual of physical objects are described.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although referring to compared with
Good embodiment describes the invention in detail, those skilled in the art should understand that, it can be to skill of the invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of the technical program, should all be covered in the present invention
Scope of the claims in.
Claims (7)
1. a kind of method for optimizing scheduling of the brick field ferry bus based on least energy consumption, it is characterised in that: the method includes following
Step:
Step S1: establishing the topology diagram of brick field movement system, is based on site layout project map analysis ferry bus operating condition, passes through
Analysis to the brick and tile product technological process of production obtains the topological structure of brick field movement system；
Step S2: it establishes model for the scheduling of double ferry buses of same track according to actual condition and determines double ferry bus fortune
Capable and task assignment constraints condition；
Step S3: with the minimum optimization object function of energy consumption, the task execution sequence of ferry bus is planned；
Step S4: the improved adaptive GA-IAGA based on parallel machine scheduling carries out the solution of double ferry bus optimizing schedulings, analyzes heredity and calculates
Mutation probability p in methodmWith crossover probability pcCalculation formula；
Step S5: according to mutation probability pmWith crossover probability pcCalculating, establish improved adaptive pmAnd pcGenetic algorithm is realized
Solution to double ferry bus optimizing schedulings.
2. a kind of method for optimizing scheduling of brick field ferry bus based on least energy consumption according to claim 1, feature exist
In: in the step S2, the scheduling problem progress model foundation for double ferry buses includes following sub-step:
Step S21: parametric assumption:
N: the number of tasks that parallel machine need to execute, wherein i={ 1,2 ..., N }；
K: parallel machine quantity；
tik: K platform machine the time it takes when executing task i；
Cik: the time of K platform machine i after having executed task；
Sik: time of the K platform machine when starting execution task i；
Zi: after task i is performed, wait pending set of tasks.Wherein Z0={ 1,2 ..., N }；
Nk: the number of tasks needed to be implemented on K platform machine；
Pkt: K platform machine is in the position of t moment；
Ti: the regulation deadline of task i；
ωi: the weight of task i, when task i fails in stipulated time TiIt is inside executed, then the consumption value of its energy need to be multiplied by power
Weight values ωi；
Eik: K platform machine has executed the energy that task i need to be consumed；
EIk: K platform machine is in unloaded kiln car, consumed energy in the idle running unit time；
Step S22: variable-definition is carried out:
Step S23: constraint condition is determined:
Cik=Sik+tikI=1,2 ..., N；K=1,2 ..., K (1)
Cik≤SrkI=1,2 ..., N；r∈Zi (2)
P2t-P1t≥L (4)
Formula (1) indicates that in task implementation procedure, the end time of current task is equal to current task Starting Executing Time and adds
Execution task itself needs the time spent；
Formula (2) indicates that current machine currently could start next task, i.e. current machine current task after execution task
Executing the end time should be no more than the job start time of the next pending task of current machine；
Formula (3) indicates that for all machines, their total execution number of tasks is N；
Formula (4) indicates that two ferry buses need certain safe distance, avoids car to car impact with this, wherein L indicates ferry-boat
The vehicle commander of vehicle.
3. a kind of method for optimizing scheduling of brick field ferry bus based on least energy consumption according to claim 2, feature exist
In: in the step S3, optimization object function takes following steps to establish:
Step S31: energy consumption E when ferry bus zero load is determinedI, i.e., ferry bus is after having executed current task until next
The consumption of energy before task starts:
Step S32: energy consumption E when ferry bus full-load run is determinedB, i.e. energy of the ferry bus in task implementation procedure disappear
Consumed energy will increase the energy of punishment formula when consuming, while needing the reason of studying because of collision avoidance and failing to complete task in time
Consumption:
ωi=a* (tik-Ti) (7)
Step S33: during the task execution of ferry bus, the wastage in bulk or weight of energy is equal to EBAnd EISum, it may be assumed that
E=EI+EB (8)
Step S34: it establishes with the objective function of the minimum optimization aim of total energy consumption:
Obj=min (E) (9)
The foundation of the objective function is that the task execution sequence of ferry bus is planned under the constraint of formula (1)~(4), so that ferry bus
Total energy consumption is minimum.
4. a kind of method for optimizing scheduling of brick field ferry bus based on least energy consumption according to claim 1, feature exist
In: in the step S4, the p of self-adapted genetic algorithmcAnd pmCalculation formula are as follows:
Wherein, fmaxFor individual in population maximum adaptation angle value；favgFor group's fitness average value；F ' is two individuals to be intersected
In larger fitness value；F is the fitness value to variation individual；Pc1And Pm1For in basic genetic algorithmic crossover probability and change
Different probability.
5. a kind of method for optimizing scheduling of brick field ferry bus based on least energy consumption according to claim 1, feature exist
In: in the step S5, the solution of double ferry bus optimizing schedulings is carried out using Revised genetic algorithum, Revised genetic algorithum
Process is as follows:
Step S51: coding mode uses real number expression way, the mission number needed to be implemented is encoded to integer variable, each
Arrangement represents a kind of task execution scheme, and each position in arrangement has just respectively represented the task of each band number；
Integer coding is used for parallel machine scheduling problem and for one-dimension array, form is as follows:
A=[a1, a2..., an] (12)
ai=z*100+j i, j ∈ { 1,2 ..., n }, z ∈ { 1,2 ..., k } (13)
Wherein, n refers to the number of tasks to be executed, and k indicates parallel number of machines, and z refers to the code name of each machine, to guarantee to execute
Task it is not repeated, there is not identical value in rear x numerical value of each element in numerical value a, can be known according to the numerical value of element
Road task j is completed by machine z, thus one-dimension array can be write as to the form of matrix, is k row n column matrix:
Step S52: it carries out the setting of initial population: generating n integer according to formula (13), form random chromosomal, complete population
Initialization；
Step S53: establish fitness function: this is using the inverse of objective function as fitness function, i.e., each individual in population
Fitness:
F (k)=1/cmax(k) (15)
Wherein, cmax(k) the maximum energy consumption value of a scheduling representated by k-th of chromosome is indicated.
Step S54: carrying out the selection of population at individual, if the individual amount of initial population is n, individual fitness function in population
Value is f (k), then the select probability of each individual are as follows:
Step S55: cross method, the random number for generating one (0,1), if the number is less than crossover probability p are establishedc, then to kind of a mass selection
With improved crossover probability pcCrossover operation is carried out, which realizes pair principle two-by-two to all individuals；
Step S56: some genic value in some individual, then the random number for generating one (0,1) are chosen at random, if the number is small
In mutation probability pm, then generate an integer at random in section [1, k] to replace the first place of the genic value, lost using adaptive
Mutation probability p in propagation algorithmmTo carry out mutation operation；
Step S57: stop criterion sets a maximum number of iterations value, which is equal to maximum number of iterations
When stop.
6. a kind of electronic equipment characterized by comprising processor, memory and bus, wherein
The processor and the memory complete mutual communication by the bus；
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program to instruct energy
Enough execute the method according to claim 1 to 5.
7. a kind of non-transient computer readable storage medium, which is characterized in that the non-transient computer readable storage medium is deposited
Computer instruction is stored up, the computer instruction makes the computer execute the method according to claim 1 to 5.
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