CN113433896B - Dynamic production scheduling method and system - Google Patents
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The invention discloses a dynamic production scheduling method and a dynamic production scheduling system. The method comprises the following steps: determining relevant factors influencing the on-time arrival of the AGV through a principal component analysis method; calling corresponding factor comparison relationship pairs according to the comparison relationship between every two factors to generate a fuzzy matrix; generating the weight of each factor by using the fuzzy matrix through a fuzzy hierarchy method; and calculating real-time uncertainty, namely dynamic uncertainty, by using the calculated weight of each factor and the field data acquired in real time, and calculating the optimal dynamic scheduling scheme of the AGV according to the uncertainty. The method dynamically generates the certainty factor according to the field interference factors, thereby dynamically adjusting the optimal route and the optimal departure time of the AGV, and ensuring that the AGV can arrive on time.
Description
Technical Field
The invention relates to the technical field of AGV trolley scheduling, in particular to a dynamic production scheduling method and a dynamic production scheduling system.
Background
The AGV (automatic guided vehicle or intelligent car) is the most popular intelligent scheduling tool at present, and is deeply favored by factories due to the functions of automatic cruising and barrier. At present, the mainstream scheduling method calls an AGV to automatically transport a required object according to temporary needs, the time waiting problem is not considered, and even if a method for sending an instruction in advance to transport the object exists, the waiting and delay in time are still caused due to the change of road conditions and field dynamics. Meanwhile, when the travel time of the AGV trolley becomes uncertain due to interference of various factors, the organization and planning of the optimal path become useless.
In the prior art, a research team of the university of Qinghua introduces an uncertain theory into the field of vehicle path research for the first time, solves the problem well by distinguishing the certainty factor of uncertain conveying time, and lays a theoretical foundation for the development of the uncertain theory in solving the problem of vehicle path organization. The basic assumption is as follows: an AGV trolley can be distributed to only one line, and a plurality of clients can be arranged on the line; a client can only be accessed by one AGV; each line starts and ends at the origin; each customer specifies a time window during which to allow for the shipment. In the scheme, the optimal path of the AGV and the optimal time point of each departure can be obtained by a genetic algorithm according to the number of the AGV trolleys and the accurate degree of arrival on time as long as the position of each production unit and the time window of the required goods are given. However, such intelligent scheduling using stochastic uncertainty theory cannot overcome the problem of dynamic changes in the field situation.
In summary, the existing scheduling algorithms optimize the total path based on a fixed certainty factor, but the certainty factor that the time arrives at the right time is interfered by various field factors, and each day or even each time is a variable value, so that the path organization based on the static certainty factor cannot meet the production requirement. In addition, the problem of punctual arrival is generally not considered in the conventional AGV scheduling, but the problem of dynamic change of field conditions cannot be solved by intelligent scheduling based on a random uncertain theory.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a dynamic production scheduling method and a dynamic production scheduling system, which consider the dynamic change factor of the field situation and solve the problem that the AGV can not arrive on time.
According to a first aspect of the present invention, a method for dynamic production scheduling is provided. The method comprises the following steps:
determining relevant factors influencing the on-time arrival of the AGV through a principal component analysis method;
calling corresponding factor comparison relationship pairs according to the comparison relationship between every two factors to generate a fuzzy matrix;
calculating the weight of each factor by using the fuzzy matrix through a fuzzy hierarchy method;
and calculating real-time uncertainty, namely dynamic uncertainty, by using the calculated weight of each factor and the field data acquired in real time, and calculating the optimal dynamic scheduling scheme of the AGV according to the uncertainty.
According to a second aspect of the invention, a dynamic production scheduling system is provided. The system comprises:
main interference factor analysis unit: the method is used for determining relevant factors influencing the on-time arrival of the AGV through a principal component analysis method;
an expert evaluation unit: the fuzzy matrix generation device is used for calling corresponding factor comparison relation pairs according to the comparison relation between every two factors to generate a fuzzy matrix;
a fuzzy level processing unit: the weight of each factor is calculated by using the fuzzy matrix through a fuzzy hierarchy method;
uncertainty calculation unit: and the method is used for calculating the real-time dynamic uncertainty by using the calculated weight of each factor and the field data acquired in real time, and calculating the optimal dynamic scheduling scheme of the AGV according to the uncertainty.
Compared with the prior art, the method has the advantages that the confidence is dynamically generated according to the field interference factors through the fuzzy mathematical algorithm, so that the optimal route and the optimal departure time of the AGV trolley are dynamically adjusted, and the AGV trolley can reach each device within the specified time.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic diagram of a dynamic production scheduling system according to one embodiment of the present invention;
FIG. 2 is a flow diagram of a method of dynamic production scheduling in accordance with one embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
In short, the technical scheme of dynamic production scheduling provided by the invention firstly determines the relationship between the site interference factors and the certainty factor, and calculates the weight of each relevant factor in the relationship, then the weight can be used for multiplying the actual factor data of the site to obtain the real-time uncertainty, when the uncertainty changes, the corresponding optimal dynamic scheduling scheme is calculated, and the AGV trolley runs according to the latest optimization scheme.
Specifically, referring to fig. 1, the provided dynamic production scheduling system includes a main interference factor analysis unit, an expert evaluation unit, a fuzzy level processing unit, and an uncertainty calculation unit.
The main interference factor analysis unit is responsible for analyzing and obtaining main interference factors, and after a plurality of relevant factors influencing the on-time arrival of the AGV are obtained through a main component analysis method, the main interference factors are transmitted to the expert evaluation unit. For example, factors that affect the on-time arrival of a car include the presence of obstacles in the path, the occurrence of traffic jams, and the like. Some interference factors with larger influence can be screened out through principal component analysis, and the follow-up analysis is realized by using fewer variables without influencing the analysis effect. The expert evaluation unit stores the comparison relationship between every two factors, and the expert evaluation unit calls the corresponding factor comparison relationship pair through the expert evaluation sheet to generate a fuzzy matrix and transmits the fuzzy matrix to the fuzzy level processing unit. The two factors are compared and judged, the importance degree of one factor to the other factor is used for quantitative representation, a fuzzy matrix mode is obtained, and the qualitative and quantitative combination can be achieved.
The fuzzy hierarchy processing unit is responsible for defuzzification, and the weight of each factor is generated and retransmitted to the uncertainty calculation unit through a fuzzy hierarchy method. The fuzzy hierarchy method provides basis for selecting the optimal path scheme for quantitative evaluation indexes, and improves decision reliability.
And the uncertainty calculation unit calculates dynamic uncertainty after collecting the weight and field data transmitted by the sensor, and optimizes an optimal scheduling scheme by using an uncertain random theory according to the value of the uncertainty. The optimal scheduling scheme includes an optimal path, an optimal time and the like, and the optimal scheduling scheme also dynamically changes along with the field situation. The optimal scheduling scheme obtained by utilizing the dynamic uncertainty can cope with the continuously changing field changes, thereby enhancing the universality of the invention.
In one embodiment, the implementation flow for optimizing a scheduling scheme using random dynamic uncertainty includes the following steps.
Step S110, a value of the dynamic certainty factor α is calculated from the sensor data.
For example, calculating dynamic confidence includes:
and step S111, formulating a fuzzy matrix of the alpha-related factors according to expert evaluation.
Step S112, defuzzification and calculation of weight W of each relevant factor according to a fuzzy hierarchy methodi。
Step S113, obtaining dynamic certainty factor alpha according to real-time detection datai(t)。
Specifically, let CijI, j ═ 1 … n as influence certainty factor αiN factors of (a) and the value VijThe real-time acquisition is carried out by a sensor, a fuzzy matrix is established by an expert system, and defuzzification calculation is carried out, so that the certainty factor alpha at the moment is solvediExpressed as:
wherein, mu (C)ij) Representing factor CijFinal weights after defuzzification.
Step S120, an optimization function is determined.
The scheduling process for an AGV is described herein as searching for the optimal solution for the optimization function in a solution set that satisfies the constraints. For example, the optimization function is set to the minimum time consumed by the optimal path for each AGV.
Specifically, the organization scheme for setting a path is composed of three decision vectors, x, y and t, which are defined as follows:
x＝(x1,x2,…,xn): is an integer vector representing n different devices ordered in sequence.
y＝(y1,y2,…,ym-1): is an integer vector representing an increasing ordering of m-1 integers, not necessarily consecutive, where y0≡0，ym≡n。
t＝(t1,t2,…,tm): representing different starting times of the m intelligent trolleys starting from the origin.
For cart k, if yk＝yk-1Indicating that the smart cart k is not in use. If y isk＞yk-1And the time when the intelligent trolley is used and starts from the original point is tkThe running route of the intelligent trolley is as follows:
let fi(x, y, t) is the time point when a certain intelligent vehicle reaches the ith device, and for the intelligent vehicle k, if the intelligent vehicle k is used, the time when the intelligent vehicle reaches the first device from the origin is as follows:
the time from the last device to the present device is:
t is the running time between nodes, which is a time distribution function related to the certainty factor α.
Let g (x, y) be the total transport distance of the smart car k, k ═ 1,2, …, m, and its expression is:
wherein the content of the first and second substances,
representing the transport path from the origin to the first device to be traversed by the trolley,
indicating the transport route between the devices and,
representing the transport path to the origin of the last piece of equipment to be traversed by the trolley.
The random dynamic optimization function is as follows:
M{fi(x,y,t)≤bi}≥αi(t)
1≤xi≤n,i＝1,2,…n
xi≠xj,i≠j,i,j＝1,2,…,n
0≤y1≤y2≤…≤ym-1≤n
wherein M represents a probability, biRepresenting the window end time set by device i.
And S130, solving an uncertain decision model based on an improved genetic algorithm of a roulette selection method.
In one embodiment, solving the uncertain decision model comprises:
step S131, initialize population, number N, chromosome code { x }1,x2,…,xn,y1,y2,…,ym,t1,t2,…,tmIs 2m + n in length, wherein x1,x2,…,xnAs service objects, y1,y2,…,ymAssigning numbers, t, to objects of different service providers1,t2,…,tmTime to start running for different service providers.
Step S132, calculating fitness f of each chromosomeiAnd cumulative probability qi,i＝1…N。
Step S133, generating offspring chromosomes through cross mutation, and calculating the fitness f of each chromosomej,j＝1…N。
Step S134, randomly selecting part of parents by the roulette method, setting the selected number as N1, selecting N2 chromosomes which are ranked earlier in the offspring by the elite method to be used as new parents together, and keeping the stability of the population size as N1+ N2 ═ N
Step S135, selecting the optimal fitness fgAnd corresponding chromosomes
Step S136, circularly running S133 to S135 until the iteration number or the optimized value is stable and unchanged
Step S137, outputting an optimal scheme comprising optimal fitness fgAnd its corresponding chromosome.
In the invention, the improved genetic algorithm is used as a search algorithm, so that the method can be more suitable for the scene of optimal path search, and one path is abstracted to one individual in the improved genetic algorithm from the individual dimension in the population; each node of a path is abstracted into genes in an individual; the performance (such as the time spent on the path) of one path is taken as the fitness of the individual, and the higher the fitness is, the better the individual is represented.
Step S140, judging whether the optimization decision scheme can meet the set requirements, if so, exiting the loop and executing the decision scheme, otherwise, entering the next step
And step S150, recommending the alternative schemes in a personalized mode according to the user preference.
Step S160, repeatedly executing S130 to S150 until exiting.
For a further understanding of the invention, the following are exemplified:
in a production workshop of enterprise B, three intelligent trolleys are responsible for supplying 6 production units. The distance between the starting point of the trolley and the production unit corresponds to the following formula:
Dij＝|i-j|i,j＝0,1,…,7
the transit time of the car follows a normal distribution, expressed as:
T～N(2|i-j|,1)i,j＝0,1,…,7
the time window for each production unit charge is seen in table 1 below. Influence certainty factor alphaiThere are four factors, emergency order rate (C1), equipment usage (C2), cart health (C3), and equipment health (C4). The fuzzy matrix between the four factors given by the expert is seen in table 2 below.
TABLE 1 time Window
| Machining unit | Time window | Machining unit | Time window |
| 1 | [6:00,8:00] | 5 | [15:00,17:00] |
| 2 | [6:00,8:00] | 6 | [18:00,20:00] |
| 3 | [11:00,13:00] | 7 | [19:00,21:00] |
| 4 | [12:00,14:00] | 8 | [21:00,23:00] |
TABLE 2 fuzzy matrix of certainty factor
| C1 | C2 | C3 | C4 | |
| C1 | (1,1,1) | (0.4,0.7,1) | (0.5,0.8,1) | (2,3,4) |
| C2 | (1,1.43,2.5) | (1,1,1) | (1,1.3,2.3) | (1.7,2.7,3.7) |
| C3 | (1,1.25,2) | (0.43,0.77,1) | (1,1,1) | (1.3,2.3,3.3) |
| C4 | (3.25,4.01,6) | (2.11,2.84,4.09) | (2.8,3.53,6.07) | (1,1,1) |
The fuzzy matrix is defuzzified by a triangular fuzzy function method, so that the final weight of Ci can be obtained, which sequentially comprises the following steps: w ═ 0.34,0.34,0.28, 0.04.
After the final weight of Ci is obtained, the expert system normalizes the relevant data V according to the data transmitted from the perception systemiTo calculate the certainty factor alphai. Assuming that all devices have the same requirements for certainty at the same time and that there is no difference in health between the various devices, the data obtained at a certain time and the α calculated by the expert system are shown in table 3 below.
Table 3 confidence value example
| V1 | V2 | V3 | V4 | α |
| 0.85 | 0.89 | 0.98 | 0.95 | 0.90 |
When the certainty factor α is 0.90, the total shortest transportation distance is 34, and the specific optimization scheme is shown in table 4 below, and the system directly pushes the scheme to the user to execute.
TABLE 4 optimization scheme
Corresponding to the system, the invention also provides a dynamic production scheduling method. For example, referring to fig. 2, the method: step S210, determining relevant factors influencing the on-time arrival of the AGV through a principal component analysis method; step S220, calling corresponding factor comparison relationship pairs according to the comparison relationship between every two factors to generate a fuzzy matrix; step S230, calculating the weight of each factor by using the fuzzy matrix through a fuzzy hierarchy method; and S240, calculating real-time uncertainty, namely dynamic uncertainty, by using the calculated weight of each factor and the field data acquired in real time, and calculating the optimal dynamic scheduling scheme of the AGV according to the uncertainty.
In conclusion, the invention combines the dynamic analysis of the main interference factors and the fuzzy hierarchical algorithm to calculate the weight of the relevant factors, and combines the real-time data values of the relevant factors to generate the dynamic uncertainty, thereby improving the applicability to the changing field. In addition, global optimization is carried out by using an improved genetic algorithm, an AGV dispatching strategy which has the shortest total transportation path and arrives on time is obtained, and balanced dispatching of all AGV trolleys is guaranteed. The invention can adapt to the change of the production field condition and ensure the on-time and reliable scheduling scheme.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (6)
1. A dynamic production scheduling method comprises the following steps:
determining relevant factors influencing the on-time arrival of the AGV through a principal component analysis method;
calling corresponding factor comparison relationship pairs according to the comparison relationship between every two factors to generate a fuzzy matrix;
calculating the weight of each factor by using the fuzzy matrix through a fuzzy hierarchy method;
calculating real-time dynamic certainty factor by using the calculated weight of each factor and the field data acquired in real time, and calculating the optimal dynamic scheduling scheme of the AGV according to the dynamic certainty factor, wherein the scheme comprises the following steps:
step S31, obtaining dynamic certainty factor alpha according to real-time detection datai(t), expressed as:
wherein, CijI, j ═ 1 … n as influence certainty factor αiN factors of (a), mu (C)ij) Representing factor CijFinal weight after defuzzification, VijDetecting data in real time, wherein t represents a time index;
step S32, establishing an optimization objective function, wherein the optimization objective function is the minimum time consumed by the path of each AGV trolley, and the constraint condition of the optimization objective function is related to the dynamic certainty factor;
step S33, solving the optimized objective function by using an improved genetic algorithm to obtain the optimal dynamic scheduling scheme of each AGV;
wherein, step S32 includes:
the one-path organization scheme comprises three decision vectors, x, y and t, where x ═ x (x)1，x2，...，xn) Is an integer vector representing n different devices ordered in sequence, y ═ y1，y2，...，ym-1) Is an integer vector, representing an ordering of m-1 integers, y0≡0，ym≡n，t＝(t1，t2，...，tm) Representing different starting time of the m AGV trolleys starting from the origin;
the travel route of the AGV cars is represented as:
wherein for AGV cart k, if yk＝yk-1Indicates not used if yk＞yk-1Is used and the time from the origin is tk；
Let fi(x, y, t) is the point in time when an AGV arrives at the ith device, and for AGV k, if used, the time from the origin to the first device is
The time from the previous device to the current device is:
t represents the running time between nodes and is a time distribution function related to the certainty factor;
establishing an optimization objective function expressed as:
xi≠xj，i≠j，i，j＝1，2，...，n
0≤y1≤y2≤…≤ym-1≤n
wherein M represents a probability, biRepresenting the end time of the window set by the facility i, g (x, y) is the total transport distance of AGV cart k, k being 1,2, …, m,
and the time of the intelligent trolley at the origin is shown.
2. The dynamic production scheduling method of claim 1, wherein the obtained optimal dynamic scheduling scheme is: the system arrives in a time window specified by each device, the total transportation distance is shortest, and the arrival time and the shortest transportation distance dynamically change along with the field condition.
3. The dynamic production scheduling method of claim 1, wherein step S33 comprises:
step S51, initialize population, number N, chromosome code { x }1，x2，...，xn，y1，y2，…，ym，t1，t2，...，tmIs 2m + n in length, wherein x1，x2，...，xnAs service objects, y1，y2，…，ymAssigning numbers, t, to objects of different service providers1，t2，...，tmTime to start running for different service providers;
step S52, calculating fitness and cumulative probability q of each chromosomei，i＝1…N；
Step S53, generating offspring chromosomes through cross mutation, and calculating the fitness f of each chromosomej，j＝1…N；
Step S54, randomly selecting part of parents by using a roulette method, setting the selected number as N1, and selecting N2 chromosomes which are ranked at the top in the offspring by using an elite method to jointly serve as new parents;
step S55, selecting the optimal fitness fgAnd its corresponding chromosome;
step S56, circularly running the steps S53 to S55 until the iteration times or the optimized value meets the set standard;
step S57, outputting the optimal dynamic scheduling scheme including the optimal fitness fgAnd its corresponding chromosome.
4. The dynamic production scheduling method of claim 3, further comprising: and carrying out personalized recommendation according to the user preference on the determined selected scheduling scheme.
5. The dynamic production scheduling method of claim 3 wherein the fitness is the time consumed by the AGV carts on the corresponding paths.
6. A dynamic production scheduling system, comprising:
main interference factor analysis unit: the method is used for determining relevant factors influencing the on-time arrival of the AGV through a principal component analysis method;
an expert evaluation unit: the fuzzy matrix generation device is used for calling corresponding factor comparison relation pairs according to the comparison relation between every two factors to generate a fuzzy matrix;
a fuzzy level processing unit: the weight of each factor is calculated by using the fuzzy matrix through a fuzzy hierarchy method;
uncertainty calculation unit: the method is used for calculating real-time dynamic certainty factor by using the calculated weight of each factor and the acquired field data, and calculating the optimal dynamic scheduling scheme of the AGV according to the dynamic certainty factor, and comprises the following steps:
step S31, obtaining dynamic certainty factor alpha according to real-time detection datai(t), expressed as:
wherein, CijI, j ═ 1 … n as influence certainty factor αiN factors of (a), mu (C)ij) Representing factor CijFinal weight after defuzzification, VijDetecting data in real time, wherein t represents a time index;
step S32, establishing an optimization objective function, wherein the optimization objective function is the minimum time consumed by the path of each AGV trolley, and the constraint condition of the optimization objective function is related to the dynamic certainty factor;
step S33, solving the optimized objective function by using an improved genetic algorithm to obtain the optimal dynamic scheduling scheme of each AGV;
wherein, step S32 includes:
the one-path organization scheme comprises three decision vectors, x, y and t, where x ═ x (x)1，x2，...，xn) Is an integer vector representing n different devices ordered in sequence, y ═ y1，y2，...，ym-1) Is an integer vector, representing an ordering of m-1 integers, y0≡0，ym≡n，t＝(t1，t2，...，tm) Representing different starting time of the m AGV trolleys starting from the origin;
the travel route of the AGV cars is represented as:
wherein for AGV cart k, if yk＝yk-1Indicates not used if yk＞yk-1Is used and the time from the origin is tk；
Let fi(x, y, t) is the point in time when an AGV arrives at the ith device, and for AGV k, if used, the time from the origin to the first device is
The time from the previous device to the current device is:
t represents the running time between nodes and is a time distribution function related to the certainty factor;
establishing an optimization objective function expressed as:
xi≠xj，i≠j，i，j＝1，2，...，n
0≤y1≤y2≤…≤ym-1≤n
wherein M represents a probability, biRepresenting the end time of the window set by the facility i, g (x, y) is the total transport distance of AGV cart k, k being 1,2, …, m,
and the time of the intelligent trolley at the origin is shown.
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| CN104036377B (en) * | 2014-06-05 | 2017-04-19 | 陕西科技大学 | Intelligent storage and distribution system optimization scheduling method based on polychromatic sets theory |
| CN106681334A (en) * | 2017-03-13 | 2017-05-17 | 东莞市迪文数字技术有限公司 | Automatic-guided-vehicle dispatching control method based on genetic algorithm |
| CN108681783A (en) * | 2018-04-04 | 2018-10-19 | 河海大学 | A kind of scheduling of reservoir real-time multi-target random optimization and methods of risk assessment |
| CN111813054B (en) * | 2019-04-11 | 2021-09-03 | 阜阳师范学院 | Dynamic scheduling method and system for rail guided vehicle |
| CN110262408A (en) * | 2019-05-08 | 2019-09-20 | 盐城品迅智能科技服务有限公司 | A kind of intelligent storage route identification device and method for more AGV |
| TWI718705B (en) * | 2019-10-15 | 2021-02-11 | 薩摩亞商紘織國際有限公司 | A system that combines dynamic production and plant material transportation management |
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