CN110910034B - Method for scheduling mixed intermittent continuous system based on bat algorithm - Google Patents
Method for scheduling mixed intermittent continuous system based on bat algorithm Download PDFInfo
- Publication number
- CN110910034B CN110910034B CN201911203387.XA CN201911203387A CN110910034B CN 110910034 B CN110910034 B CN 110910034B CN 201911203387 A CN201911203387 A CN 201911203387A CN 110910034 B CN110910034 B CN 110910034B
- Authority
- CN
- China
- Prior art keywords
- bat
- production
- time
- migration
- scheduling
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 86
- 238000004519 manufacturing process Methods 0.000 claims abstract description 80
- 238000002939 conjugate gradient method Methods 0.000 claims abstract description 9
- 238000013508 migration Methods 0.000 claims description 42
- 230000005012 migration Effects 0.000 claims description 39
- 238000010924 continuous production Methods 0.000 claims description 19
- 230000000977 initiatory effect Effects 0.000 claims description 11
- 241000288673 Chiroptera Species 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 230000002068 genetic effect Effects 0.000 claims description 3
- 239000013067 intermediate product Substances 0.000 claims description 3
- 230000001960 triggered effect Effects 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 239000000047 product Substances 0.000 description 9
- 230000008569 process Effects 0.000 description 7
- 230000006870 function Effects 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 230000011218 segmentation Effects 0.000 description 4
- 230000000052 comparative effect Effects 0.000 description 3
- 238000012432 intermediate storage Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003094 perturbing effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- General Health & Medical Sciences (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Quality & Reliability (AREA)
- Evolutionary Computation (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Agronomy & Crop Science (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for scheduling a hybrid intermittent continuous system based on a bat algorithm, which comprises the following steps: s1, defining production parameters by adopting a hybrid Petri network, and setting a production target and production limiting parameters; s2, initializing basic parameters of the bat algorithm and Levy flight scale parameters; s3, initializing a bat position, recording the bat position into a taboo table, and calculating the optimal bat position reached by the current production target; s4, adopting Levy flight mode to calculate the new bat position of each bat, calculating to obtain another new bat position on the basis of the new bat position by adopting conjugate gradient method, and obtaining the new bat position; s5, if the production target result of the bat new position is better than S3, the optimal bat position is updated; s6, adjusting riAnd AiRepeating the steps S3-S5 until the number of searching times reaches Nmax. The scheduling method has the beneficial effect of quickly and efficiently obtaining a feasible scheduling method with a better target result.
Description
Technical Field
The present invention relates to the field of scheduling of promiscuous intermittent continuous systems. More particularly, the invention relates to a scheduling method of a hybrid intermittent continuous system based on a bat algorithm.
Background
Description and scheduling of production systems has been an important area of research in industry and manufacturing, and various methods have been proposed to achieve description and optimal scheduling of continuous or intermittent production systems. However, the lack of research effort in the description of hybrid batch and continuous systems and their scheduling is primarily due to the fact that their hybrid characteristics, which include both discrete and continuous operation and their interaction, are difficult to describe and analyze mathematically. Discretizing the continuous operation of the hybrid intermittent and continuous system or continuously transforming the discrete operation of the hybrid intermittent and continuous system into a discrete model or a continuous model, wherein the method for realizing the scheduling of the hybrid intermittent and continuous system by using the traditional method is a main method for researching the problem at present, for example, describing an intermittent production process by using a state-task network and then realizing the short-period scheduling of the production process by using discrete time mixed integer programming; for example, a short-period scheduling method of a multi-stage intermittent production process is given based on a continuous time mixed integer linear programming model; aiming at the problem of scheduling performance of the hybrid intermittent and continuous production process based on the linear programming method, a scheduling algorithm based on an optimization control and layer segmentation technology is also provided in the prior art, and the performance of the scheduling algorithm is compared with the performance of the linear programming method based on mixed integers. From the above studies, it was found that a hybrid intermittent and continuous production system with hybrid characteristics is a very important problem, but there are still many problems and technical needs to be intensively studied on how to effectively describe such a problem and reduce the computational complexity of the solution scheduling. In view of the fact that the Petri net has been recognized as one of the effective technologies for solving the scheduling problem, there is a literature that proposes a hybrid intermittent and continuous production system scheduling method based on a hybrid time Petri net behavior evolution method, which does not need to divide the production time of the production system, nor does it need to discretize continuous operations or to continue discrete operations. But only one feasible scheduling solution for a promiscuous intermittent and continuous system is found instead of its optimal scheduling solution or its near-optimal solution.
The Bat Algorithm (Bat Algorithm, BA for short) is a novel heuristic Algorithm proposed in 2010 by the inspiration of the Bat echo positioning behavior of Xin-She Yang. Since the self-algorithm is provided, researchers apply the algorithm to the scheduling problem of a single-target flexible job workshop, the multi-target vehicle path problem, the maneuvering target tracking and the self-adaptive evolution, and experiments show that the optimal solution or the approximately optimal solution of the problem can be effectively solved by using the bat algorithm and the time complexity is low. Aiming at the defects that the algorithm is easy to fall into local optimum, shock occurs, the convergence speed is slow and the like, researchers improve the basic bat algorithm proposed by Xin-She Yang. A speed and position updating mode of an original algorithm is replaced by a Levy flight searching strategy, and attraction of local extreme values is effectively avoided.
The system comprises a plurality of intermittent units, a middle storage tank for storing intermittent production products, a continuous production subsystem and the like. Wherein the production run time of the batch unit is variable, i.e. the batch run time may be a range of time values rather than a fixed constant. The quantity of product in the intermediate storage tanks for storing the intermittently produced product needs to be kept within a specified quantity range. The continuous operating rate of the continuous production unit is also variable, requiring that its operating rate be greater than or equal to a specified minimum operating rate and less than or equal to a specified maximum operating rate. If a production schedule of the hybrid discontinuous continuous system is satisfied within the specified production time range TH: (1) each intermittent operation can be completed within the specified operation time interval; (2) at any time in TH, the continuous operating rate is between its prescribed minimum and maximum operating rates; (3) at any time in TH, the number of products in the intermediate storage tank is maintained between its specified minimum and maximum product numbers; the schedule is one feasible schedule within TH for the promiscuous intermittent continuous system.
Disclosure of Invention
An object of the present invention is to solve at least the above problems and to provide at least the advantages described later.
The invention also aims to provide a scheduling method of the hybrid intermittent continuous system based on the bat algorithm, and firstly, a hybrid Petri network model for effectively describing the hybrid intermittent continuous system is defined. And then, converting the optimal scheduling solution to the optimal time sequence by utilizing the corresponding relation between the hybrid Petri network region state sequence and the time sequence.
To achieve these objects and other advantages in accordance with the purpose of the invention, there is provided a scheduling method of a promiscuous intermittent continuous system based on a bat algorithm, including:
s1, defining production parameters by adopting a hybrid Petri network, and setting a production target and production limiting parameters;
s2, initializing basic parameters of the bat algorithm and Levy flight scale parameters, wherein the basic parameters comprise: m bats group number, i bats individual, maximum pulse frequency ri 0Pulse intensity riAnd maximum pulse intensity AiFrequency increasing coefficient gamma and sound intensity attenuation coefficient alpha, and Levy flight scale parameters comprise: wavelength λ, maximum number of iterations NmaxSearching precision epsilon;
s3, randomly initializing a bat position for each bat individual i, judging whether a time sequence corresponding to the bat position meets set production limit parameters, if so, recording the bat position into a taboo table, and calculating a bat position with an optimal production target in the current taboo table as a current optimal bat position and a corresponding bat individual;
s4, generating a random number R1If R is1<riIf the new bat position is not recorded in a taboo table, another new bat position is calculated on the basis of the new bat position by adopting a conjugate gradient method, and the another new bat position is the current bat new position of each bat individual;
s5, generating a random number R2If R is2＜AiAnd the production target result of the bat new position is better than the production target result of the optimal bat position determined in the step S3, the optimal bat position and the corresponding bat individual are updated;
s6, adjusting the pulse intensity riAnd maximum pulse intensity AiRepeating the steps S3-S5 until the search times reach the maximum iteration times NmaxThen, the optimal bat position and the corresponding bat individual obtained in the step S5 are the optimal scheduling method.
Preferably, in step S3, if the time series is invalid, the invalid time series is evolved into a valid time series by a genetic algorithm.
Preferably, in step S4, if R is1≥riRandomly perturbing until generating a new bat position not recorded in a tabu table, calculating to obtain another new bat position based on the new bat position by adopting a conjugate gradient method,the other new bat position is the current bat new position of each bat individual.
Preferably, in step S4, if the new bat position exists in the tabu table, the iteration is ended, and the step S3 is executed again.
Preferably, in step S5, if R is2≥AiThen step S6 is executed.
Preferably, in step S6, the pulse intensity r is adjustediAnd maximum pulse intensity AiAre respectively shown in formula (1) and formula (2):
ri t+1＝ri 0[1-exp(-γ×t)]formula (1)
Ai t+1＝α×Ai tFormula (2)
Wherein r isi t+1Representing the pulse frequency of the bat at time t + 1; a. thei tRepresenting the sound intensity of the bat i emitted pulse at time t.
Preferably, the hybrid Petri net in step S1 is a seven-tuple model, and the seven-tuple model specifically is: IHPN ═ P, T, F, W, S, F), where,
p comprises a plurality of libraries P of intermittent production unitsdAnd a plurality of continuous production cell libraries PcA set of (a);
t comprises an intermittent production unit migration set TdContinuous production unit migration set TcEnable migration set TiMigration set T that must be initiated within a specified time frameDA set of (a);
f is a set formed by arcs between all the hybrid Petri network libraries and the migration;
W:(P×Td)∪(Td×P)→Z+as a function of the weight of the arc, Z+Is a positive integer set;
S：TD→(R+∪{0})×(R+u {0}), as a function of the time interval of the delay of the time interval migration, R+Is a set of positive integers, and is,
let S (t)i)＝(mindi,maxdi) Wherein mindi≤maxdi，mindiFor minimum initiation delay after migration Enable, maxdiMaximum initiation delay after migration enable;
f is the set of all continuous migration initiation rate intervals, and any continuous migration tiAll correspond to a rate interval [ V ]i min,Vi max]And specifies tiVelocity v at initiationiMust satisfy Vi min≤vi≤Vi max；
Is provided with<IHPN,m>For interval hybrid Petri net with initial mark m, if any input base p of discrete migration tiIs identified by the libraryiGreater than or equal to W (p)iT), then t is called enabled discrete migration;
is provided with<IHPN,m>For interval hybrid Petri net with initial mark m, if any one of t input discrete library places p is continuously migratediIs identified by the libraryiGreater than or equal to W (p)iT) while any of the continuously migrated entries is entered into a continuous library piIs identified by the libraryi>0, then t is called enabled continuous migration;
let the starting time of an RS state be taukFor a given time value τk+1If τ isk+1If the conditions (1), (2), (3) and (4) are satisfied, τ isk+1For the time effective to change the state of the RS, the conditions are as follows:
Vj min≤vc(τk)≤Vj maxcondition (2)
Presence of Tfe(τk+1) Is not empty subset TfSo that
Satisfies ET (t)j)+mindj≤τk+1(ii) a Condition (4).
Preferably, the production target is the maximum continuous production yield and the required time within the set time TH.
Preferably, the production limitation parameters include a time range per operation of each intermittent unit, a continuous operation rate range of each connected unit, and an intermediate product storage range.
The invention at least comprises the following beneficial effects:
first, a hybrid Petri net model is defined that effectively describes a hybrid discontinuous continuous system. And then, converting the optimal scheduling solution to the optimal time sequence by utilizing the corresponding relation between the hybrid Petri network region state sequence and the time sequence. An effective time sequence judgment algorithm is provided according to the semantics of the hybrid Petri network, and a method for converting an invalid time sequence into an effective time sequence is provided at the same time. An improvement method of the bat algorithm is provided by combining strategies based on Levy flight, tabu table search, conjugate gradient method and the like.
Secondly, aiming at the problem of the scheduling optimization method of the existing hybrid intermittent continuous system, the invention organically integrates the hybrid Petri network behavior evolution and the bat algorithm of the hybrid intermittent continuous system, and provides a novel optimal scheduling method of the hybrid intermittent continuous system. Compared with an optimal control and layer segmentation method (NHP method for short) and a behavior evolution method (BE method for short), along with the increase of production time, the yield obtained by the scheduling method based on the bat algorithm is obviously more than that obtained by the optimal control and layer segmentation method, and the production time is shorter.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
FIG. 1 is an IHPN model of a promiscuous intermittent continuous system in accordance with one aspect of the present invention;
FIG. 2 is a RS state sequence diagram of a hybrid Petri network model according to one embodiment of the present invention;
FIG. 3 is a Gantt chart of batch operation of example 1 of the present invention;
FIG. 4 is a graph of the continuous operation rate of example 1 of the present invention;
FIG. 5 is a graph showing a change in the amount of a product stored in a tundish according to example 1 of the present invention;
FIG. 6 is a graph comparing the yields of three processes of comparative example of the present invention;
FIG. 7 is a graph comparing the scheduling times of the three methods of the comparative example of the present invention;
fig. 8 is a flowchart of the scheduling method according to one embodiment of the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1 to 8, the present invention provides a method for scheduling a hybrid intermittent continuous system based on a bat algorithm, comprising:
s1, defining production parameters by adopting a hybrid Petri network, and setting a production target and production limiting parameters; different production targets are set according to production requirements, for example, the production target is set as the production yield in a certain time range, and the larger the production yield is, the better the result is. Production limit parameters such as the range of the operating speed of the equipment, the capacity of the intermediate transfer station, the necessary duration of a production process, etc. are set according to the production equipment, the location, the process.
S2, initializing basic parameters of the bat algorithm and Levy flight scale parameters, wherein the basic parameters comprise: m bats group number, i bats individual, maximum pulse frequency ri 0Pulse intensity riAnd maximum pulse intensity AiFrequency increasing coefficient gamma and sound intensity attenuation coefficient alpha, and Levy flight scale parameters comprise: wavelength λ, maximum number of iterations NmaxSearch accuracy epsilon；
S3, randomly initializing a bat position for each bat individual i, namely randomly obtaining a plurality of production scheduling methods, judging whether a time sequence corresponding to the bat position meets set production limiting parameters, if so, recording the bat position into a taboo table, and calculating a bat position with an optimal production target in the current taboo table as a current optimal bat position and a corresponding bat individual; and screening out the optimal one of the multiple production scheduling methods randomly obtained at present.
S4, generating a random number R1If R is1<riIf the new bat position is not recorded in a taboo table, another new bat position is calculated on the basis of the new bat position by adopting a conjugate gradient method, and the another new bat position is the current bat new position of each bat individual; in the basic bat algorithm, the updated bat position directly enters the next iteration process; in the improved bat algorithm, the bat position updated by Levy flight is not directly subjected to the next iteration, but the gradient of the bat position updated by Levy flight is multiplied by a conjugate factor and then added to the negative gradient of the point to construct a group of conjugate directions, the new position of the bat is searched along the group of directions, and then the next iteration is performed, so that the local optimization capability of the algorithm is improved, and the capability of searching the optimal production scheduling scheme is improved.
S5, generating a random number R2If R is2＜AiAnd the production target result of the bat new position is better than the production target result of the optimal bat position determined in the step S3, the optimal bat position and the corresponding bat individual are updated;
s6, adjusting the pulse intensity riAnd maximum pulse intensity AiRepeating the steps S3-S5 until the search times reach the maximum iteration times NmaxThen, the optimum bat obtained in step S5The position and the corresponding bat individual are the optimal scheduling method.
In the technical scheme, firstly, a hybrid Petri net model for effectively describing a hybrid intermittent continuous system is defined. And then, converting the optimal scheduling solution to the optimal time sequence by utilizing the corresponding relation between the hybrid Petri network region state sequence and the time sequence. The bat algorithm is improved by combining strategies based on Levy flight, tabu table search, conjugate gradient method and the like, so that the local optimization capability of the algorithm is improved, the capability of searching the optimal production scheduling scheme is improved, and the calculated production scheduling scheme is closer to the optimal scheme in the ideal process.
In another embodiment, in step S3, if the time sequence is invalid, the invalid time sequence is evolved into a valid time sequence by a genetic algorithm. The corresponding bat individual can reach the effective bat position, namely the production scheduling method is effective, and the method can be really realized and actually operated under the set production limit parameters.
In another technical solution, in step S4, if R is present1≥riAnd randomly disturbing until generating a new bat position which is not recorded in a tabu table, and calculating to obtain another new bat position on the basis of the new bat position by adopting a conjugate gradient method, wherein the another new bat position is the current bat new position of each bat body.
In another technical solution, in step S4, if there is the new bat position in the tabu table, the iteration is ended, and step S3 is executed again. Recalculation seeks the optimal production scheduling method.
In another technical solution, in step S5, if R is present2≥AiThen step S6 is executed. Recalculation, and entering the next round of loop for seeking the optimal bat position.
In another technical solution, in step S6, the pulse intensity r is adjustediAnd maximum pulse intensity AiAre respectively shown in formula (1) and formula (2):
ri t+1＝ri 0[1-exp(-γ×t)]publicFormula (1)
Ai t+1＝α×Ai tFormula (2)
Wherein r isi t+1Representing the pulse frequency of the bat at time t + 1; a. thei tRepresenting the sound intensity of the bat i emitted pulse at time t.
In another technical solution, the hybrid Petri net in step S1 is a seven-tuple model, and the seven-tuple model specifically includes: IHPN ═ P, T, F, W, S, F), where,
p comprises a plurality of libraries P of intermittent production unitsdAnd a plurality of continuous production cell libraries PcA set of (a);
t comprises an intermittent production unit migration set TdContinuous production unit migration set TcEnable migration set TiMigration set T that must be initiated within a specified time frameDA set of (a);
f is a set formed by arcs between all the hybrid Petri network libraries and the migration;
W:(P×Td)∪(Td×P)→Z+as a function of the weight of the arc, Z+Is a positive integer set;
S：TD→(R+∪{0})×(R+u {0}), as a function of the time interval of the delay of the time interval migration, R+Is a set of positive integers, and is,
let S (t)i)＝(mindi,maxdi) Wherein mindi≤maxdi，mindiFor minimum initiation delay after migration Enable, maxdiMaximum initiation delay after migration enable;
f is the set of all continuous migration initiation rate intervals, and any continuous migration tiAll correspond to a rate interval [ V ]i min,Vi max]And specifies tiVelocity v at initiationiMust satisfy Vi min≤vi≤Vi max；
Is provided with<IHPN,m>For interval hybrid Petri net with initial mark m, if any input base p of discrete migration tiIs identified by the libraryiGreater than or equal to W (p)iT), then t is called enabled discrete migration;
is provided with<IHPN,m>For interval hybrid Petri net with initial mark m, if any one of t input discrete library places p is continuously migratediIs identified by the libraryiGreater than or equal to W (p)iT) while any of the continuously migrated entries is entered into a continuous library piIs identified by the libraryi>0, then t is called enabled continuous migration;
let the starting time of an RS state be taukFor a given time value τk+1If τ isk+1If the conditions (1), (2), (3) and (4) are satisfied, τ isk+1For the time effective to change the state of the RS, the conditions are as follows:
Vj min≤vc(τk)≤Vj maxcondition (2)
Presence of Tfe(τk+1) Is not empty subset TfSo that
Satisfies ET (t)j)+mindj≤τk+1(ii) a Condition (4).
In the technical scheme, the steps of describing production parameters by using a seven-element group model and seeking the optimal production scheduling method are provided, and the production parameters can be divided according to the inherent properties of intermittent continuous production. Such as the compartmentalized hybrid Petri nets model of a hybrid intermittent continuous production system with three intermittent production units as depicted in fig. 1, the three intermittent operation time compartments of the system are [3,6], [5,8] and [4,6], respectively, with corresponding storage capacities of 8, 10 and 10, respectively.
As shown in fig. 2, a feasible scheduling solution of the hybrid discontinuous continuous production system based on the hybrid Petri net model is actually a behavior evolution process of the hybrid Petri net model, the evolution process is composed of several levels of region states (regional states) and events for changing the region states (hereinafter referred to as RS state sequences), and each region state is composed of the initiation rate of the library identification and migration.
In another embodiment, the production target is the maximum continuous production throughput and the required time within the set time TH.
In another embodiment, the production limitation parameters include a time range for each operation of each intermittent unit, a continuous operation rate range of each continuous unit, and an intermediate product storage range.
< example >
The optimal scheduling problem for a promiscuous intermittent continuous system as described in figure 1. The experimental hardware environment is P4-1.7G PC and 512M memory; the software environment is WinXP + Visual C + +. The main parameter settings of the bat algorithm for optimal scheduling of the hybrid intermittent continuous system are as follows: ri0 ═ 0.75, Ai ═ 0.75, γ ═ 0.037, α ═ 0.97, λ ═ 1.7, and the maximum number of iterations is 3000. The correctness of the method presented here is first verified, without giving the sequence and timing of the operation of the batch unit of the hybrid batch continuous system, the variation of the continuous operation rate and the variation of the quantity of product in the intermediate storage tank in the case of a production time TH of 30. Fig. 3 shows the operation sequence and time of each intermittent unit, and it can be seen that each operation time of each intermittent unit is within the specified operation time range. Fig. 4 shows the variation of the continuous operation rate in the time range TH of 30, and the data shows that the minimum operation rate is greater than 2.5 and the maximum operation rate is not greater than 12 in the specified time range. Fig. 5 shows the variation law of the product value in the intermediate tank, and the data variation shows that the product value in the intermediate tank is in the specified specification which cannot be less than 4 and cannot be greater than 26 in the time range TH of 30. It is demonstrated that the algorithm presented herein is able to correctly implement the scheduling of a hybrid intermittent continuous production system.
< comparative example >
In order to verify the effectiveness of the method proposed herein (abbreviated as HPN-BA method by combining hybrid Petri nets and bat algorithms), it is analyzed by comparing it with an optimal control and layer segmentation method (abbreviated as NHP method) and a behavior evolution method (abbreviated as BE method), and the maximum continuous production yield and the time spent by the three methods when the production time TH is 100, 300, 500, 800, 1000, 1200, 1500, 1800, 2000, 2500 time units are respectively examined, as shown in fig. 6 and fig. 7.
It can BE seen from fig. 5 that the BE method can implement scheduling of the hybrid intermittent continuous system in different production times and the scheduling time is short, but because the method can only find a unique feasible scheduling solution, the obtained production yield is difficult to reach an optimal solution or an approximately optimal solution, but with the increase of TH, the yield value obtained by the BE method is more and more distant from the yield values obtained by the NHP method and the HPN-BA method herein. The yield obtained by the HPN-BA method herein is not larger than that obtained by the NHP method at a smaller TH value of the production time than that obtained by the NHP method, but the yield obtained by the HPN-BA method is significantly larger than that obtained by the NHP method as the TH value increases. Compared with the BE method, the HPN-BA method and the NHP method require optimal scheduling in a feasible scheduling space, unlike the BE method which requires only one feasible scheduling solution, so the two methods take more time and are consistent with the data shown in fig. 7. However, the BE method has difficulty meeting the scheduling objective as measured by the maximum throughput value as the optimal scheduling objective, and for example, with TH being 1000, the throughput value of the BE method differs from that of the HPN-BA method by 958, which is larger as TH increases. From the time taken, the time taken by the HPN-BA method is not advantageous in comparison with the NHP method in that the TH value of the production time is small, but the HPN-BA method is scheduled for a shorter time than the NHP method as the TH value increases. Thus, the bat algorithm-based scheduling method proposed herein is more effective when the production time TH value is gradually increased, relative to the two methods compared.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (8)
1. The method for scheduling the hybrid intermittent continuous system based on the bat algorithm is characterized by comprising the following steps:
s1, defining production parameters by adopting a hybrid Petri network, and setting a production target and production limiting parameters;
s2, initializing basic parameters of the bat algorithm and Levy flight scale parameters, wherein the basic parameters comprise: m bats group number, i bats individual, maximum pulse frequency ri 0Pulse intensity riAnd maximum pulse intensity AiFrequency increasing coefficient gamma and sound intensity attenuation coefficient alpha, and Levy flight scale parameters comprise: wavelength λ, maximum number of iterations NmaxSearching precision epsilon;
s3, randomly initializing a bat position for each bat individual i, judging whether a time sequence corresponding to the bat position meets set production limit parameters, if so, recording the bat position into a taboo table, and calculating a bat position with an optimal production target in the current taboo table as a current optimal bat position and a corresponding bat individual;
s4, generating a random number R1If R is1<riCalculating a new bat position of each bat individual by adopting a Levy flight mode, judging whether a time sequence corresponding to the new bat position meets set production limit parameters, if so, the time sequence is effective, and if the new bat position is not recorded in a taboo table, calculating to obtain another new bat position on the basis of the new bat position by adopting a conjugate gradient method, wherein the another new bat position is the current bat position of each bat individualA new bat position;
s5, generating a random number R2If R is2＜AiAnd the production target result of the bat new position is better than the production target result of the optimal bat position determined in the step S3, the optimal bat position and the corresponding bat individual are updated;
s6, adjusting the pulse intensity riAnd maximum pulse intensity AiRepeating the steps S3-S5 until the search times reach the maximum iteration times NmaxThen, the optimal bat position and the corresponding bat individual obtained in the step S5 are the optimal scheduling method;
the hybrid Petri network in the step S1 is a seven-tuple model, and the seven-tuple model specifically comprises: IHPN ═ P, T, F, W, S, F), where,
p comprises a plurality of libraries P of intermittent production unitsdAnd a plurality of continuous production cell libraries PcA set of (a);
t comprises an intermittent production unit migration set TdContinuous production unit migration set TcEnable migration set TiMigration set T that must be initiated within a specified time frameDA set of (a);
f is a set formed by arcs between all the hybrid Petri network libraries and the migration;
W:(P×Td)∪(Td×P)→Z+as a function of the weight of the arc, Z+Is a positive integer set;
S：TD→(R+∪{0})×(R+u {0}), as a function of the time interval of the delay of the time interval migration, R+Is a set of positive integers, and is,
let S (t)i)＝(mindi,maxdi) Wherein mindi≤maxdi，mindiFor minimum initiation delay after migration Enable, maxdiMaximum initiation delay after migration enable;
f is the set of all continuous migration initiation rate intervals, and any continuous migration tiAll correspond to a rate interval [ V ]i min,Vi max]And specifies tiVelocity v at initiationiMust satisfy Vi min≤vi≤Vi max；
Is provided with<IHPN,m>For interval hybrid Petri net with initial mark m, if any input base p of discrete migration tiIs identified by the libraryiGreater than or equal to W (p)iT), then t is called enabled discrete migration;
is provided with<IHPN,m>For interval hybrid Petri net with initial mark m, if any one of t input discrete library places p is continuously migratediIs identified by the libraryiGreater than or equal to W (p)iT) while any of the continuously migrated entries is entered into a continuous library piIs identified by the libraryi>0, then t is called enabled continuous migration;
let the starting time of an RS state be taukFor a given time value τk+1If τ isk+1If the conditions (1), (2), (3) and (4) are satisfied, τ isk+1For the time effective to change the state of the RS, the conditions are as follows:
Vj min≤vc(τk)≤Vj maxcondition (2)
Presence of Tfe(τk+1) Is not empty subset TfSo that
Satisfies ET (t)j)+mindj≤τk+1(ii) a Condition (4);
wherein, Vj minLower limit of continuous rate interval, Vj maxUpper limit of continuous rate interval, vc(τk) Is taukContinuous migration initiation rate of time, Tfe(τk+1) Is tauk+1Time-triggered time interval migration set, TfIs Tfe(τk+1) Is not empty, ET (t)j) Is tjThe enable time of (2).
2. The method for scheduling of a promiscuous intermittent continuous system based on bat algorithm as claimed in claim 1, characterized in that in step S3, if the time series is invalid, the invalid time series is evolved into valid time series by genetic algorithm.
3. The method for scheduling of a hybrid intermittent continuous system based on bat algorithm as claimed in claim 1, wherein in step S4, if R is1≥riAnd randomly disturbing until generating a new bat position which is not recorded in a tabu table, and calculating to obtain another new bat position on the basis of the new bat position by adopting a conjugate gradient method, wherein the another new bat position is the current bat new position of each bat body.
4. The bats algorithm-based scheduling method for a promiscuous intermittent continuous system as claimed in claim 1, wherein in step S4, if there is the new bats position in the tabu list, the iteration is ended and the step S3 is executed again.
5. The method for scheduling of a hybrid intermittent continuous system based on bat algorithm as claimed in claim 1, wherein in step S5, if R is2≥AiThen step S6 is executed.
6. The method for scheduling of a hybrid intermittent continuous system based on a bat algorithm as claimed in claim 1, wherein in step S6, the pulse intensity r is adjustediAnd maximum pulse intensity AiRespectively, as shown in formula (1) And formula (2):
ri t+1＝ri 0[1-exp(-γ×t)]formula (1)
Ai t+1＝α×Ai tFormula (2)
Wherein r isi t+1Representing the pulse frequency of the bat at time t + 1; a. thei tRepresenting the sound intensity of the bat i emitted pulse at time t.
7. The method for scheduling of a hybrid intermittent continuous system based on a bat algorithm as claimed in claim 1, wherein the production target is a maximum continuous production yield and a required time within a set time TH.
8. The method for scheduling of a hybrid intermittent continuous system based on a bat algorithm as claimed in claim 1, wherein the production limitation parameters comprise time per operation range of each intermittent unit, continuous operation rate range of each connected unit, intermediate product storage range.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911203387.XA CN110910034B (en) | 2019-11-29 | 2019-11-29 | Method for scheduling mixed intermittent continuous system based on bat algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911203387.XA CN110910034B (en) | 2019-11-29 | 2019-11-29 | Method for scheduling mixed intermittent continuous system based on bat algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110910034A CN110910034A (en) | 2020-03-24 |
| CN110910034B true CN110910034B (en) | 2022-04-22 |
Family
ID=69821052
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911203387.XA Active CN110910034B (en) | 2019-11-29 | 2019-11-29 | Method for scheduling mixed intermittent continuous system based on bat algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110910034B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110598941A (en) * | 2019-09-18 | 2019-12-20 | 河海大学常州校区 | Bionic strategy-based dual-target scheduling method for particle swarm optimization manufacturing system |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3239864A1 (en) * | 2016-04-28 | 2017-11-01 | Schneider Electric Industries SAS | Method for generating a petri net simulation model of an industrial control system |
| CN107579518B (en) * | 2017-09-15 | 2019-02-26 | 山东大学 | Power system environment economic load dispatching method and apparatus based on MHBA |
| CN108932566A (en) * | 2018-07-19 | 2018-12-04 | 重庆邮电大学 | Based on the method for improving bat algorithm solution electric system multiple target active power dispatch |
| CN110232212B (en) * | 2019-05-08 | 2021-06-29 | 上海交通大学 | Antenna design optimization method and system based on bat algorithm |
-
2019
- 2019-11-29 CN CN201911203387.XA patent/CN110910034B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110598941A (en) * | 2019-09-18 | 2019-12-20 | 河海大学常州校区 | Bionic strategy-based dual-target scheduling method for particle swarm optimization manufacturing system |
Non-Patent Citations (1)
| Title |
|---|
| Fault Diagnosis of Transformer Based on Chaotic Bats Algorithm Optimizing Fuzzy Petri Net;Mengjia Li et al.;《2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)》;20180924;第885-889页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110910034A (en) | 2020-03-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107168267B (en) | Based on the production scheduled production method and system for improving population and heuristic strategies | |
| CN113544599A (en) | Method for executing a process and optimizing a control signal used in the process | |
| Arumugam et al. | A novel and effective particle swarm optimization like algorithm with extrapolation technique | |
| US20190220750A1 (en) | Solution search processing apparatus and solution search processing method | |
| CN113033970B (en) | AUV cluster layered distribution method for large-scale tasks | |
| CN114895690A (en) | Robot path planning method and system based on dynamic weighting and thermodynamic diagram algorithm | |
| CN110910034B (en) | Method for scheduling mixed intermittent continuous system based on bat algorithm | |
| CN108876029A (en) | A kind of passenger flow forecasting based on the adaptive chaos firefly of double populations | |
| Lam et al. | An efficient simulated annealing schedule: implementation and evaluation | |
| CN113919217A (en) | Self-adaptive parameter setting method and device for active disturbance rejection controller | |
| CN112731888A (en) | Improved migrant bird optimization method for scheduling problem of batch flow mixed flow shop | |
| CN112462704A (en) | Mixed flow batch scheduling optimization method for sensor workshop production | |
| Kalandros et al. | Randomization and super-heuristics in choosing sensor sets for target tracking applications | |
| CN111488208B (en) | Bian Yun collaborative computing node scheduling optimization method based on variable-step-size bat algorithm | |
| CN109634732A (en) | The resource regulating method based on genetic algorithm is imaged for ISAR | |
| Ying et al. | Hybrid-directional planning: improving improvement heuristics for scheduling resource-constrained projects | |
| Nouri et al. | A discrete artificial bee colony algorithm for resource-constrained project scheduling problem | |
| Nieto et al. | Threshold-crossing time statistics for size-dependent gene expression in growing cells | |
| CN116061182A (en) | Mechanical arm track planning method based on improved chicken flock optimization algorithm | |
| CN115759455A (en) | Load probability density prediction method based on time sequence Gaussian mixture density network | |
| CN113869648A (en) | MIMO radar resource scheduling method based on PSO optimization algorithm | |
| CN114489035B (en) | Multi-robot collaborative search method based on accumulated trace reinforcement learning | |
| CN106611215A (en) | Novel cuckoo search algorithm for solving job-shop scheduling problem | |
| Li et al. | Optimal design of high‐performance rare‐earth‐free wrought magnesium alloys using machine learning | |
| Siva et al. | Robust multiplexed MPC for distributed multi-agent systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |