CN110046810B - Multi-target scheduling method for workshop manufacturing system based on timed Petri network - Google Patents
Multi-target scheduling method for workshop manufacturing system based on timed Petri network Download PDFInfo
- Publication number
- CN110046810B CN110046810B CN201910261488.6A CN201910261488A CN110046810B CN 110046810 B CN110046810 B CN 110046810B CN 201910261488 A CN201910261488 A CN 201910261488A CN 110046810 B CN110046810 B CN 110046810B
- Authority
- CN
- China
- Prior art keywords
- node
- list
- target
- nodes
- child
- 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.)
- Expired - Fee Related
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 28
- 230000007704 transition Effects 0.000 claims abstract description 42
- 239000011159 matrix material Substances 0.000 claims abstract description 32
- 238000010845 search algorithm Methods 0.000 claims abstract description 8
- 230000008569 process Effects 0.000 claims description 5
- 230000001960 triggered effect Effects 0.000 claims description 4
- 230000001174 ascending effect Effects 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000012216 screening Methods 0.000 claims description 2
- 238000004422 calculation algorithm Methods 0.000 abstract description 7
- 238000002474 experimental method Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 239000000872 buffer Substances 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012905 input function Methods 0.000 description 3
- 238000005094 computer simulation Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012821 model calculation Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013329 compounding Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012546 transfer Methods 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
- 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
- G06Q10/067—Enterprise or organisation modelling
-
- 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
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention discloses a multi-target scheduling method of a workshop manufacturing system based on a timed Petri network, which comprises the following steps: modeling the multi-target workshop manufacturing system by using a Petri network; reading attribute values corresponding to all libraries in the Petri network model, and solving a correlation matrix between the libraries and the transition in the Petri network model; and based on the incidence matrix and an A-search algorithm, expanding the child nodes from the initial node until all target nodes are found, namely completing the multi-target scheduling of the system. The method takes a timed Petri network model of a workshop manufacturing system as an object, adopts a multi-objective heuristic scheduling method, finds out a non-dominated scheduling scheme which best meets requirements through comprehensive judgment of a plurality of attributes of the target, can obtain all non-dominated solutions aiming at different attributes, adopts a heuristic multi-objective A algorithm, and can obtain the system scheduling scheme meeting the requirements without expanding all nodes of the system.
Description
Technical Field
The invention belongs to the field of modeling and heuristic scheduling of a workshop manufacturing system, and particularly relates to a multi-target scheduling method of the workshop manufacturing system based on a timed Petri network.
Background
The shop manufacturing system consists of limited resources that must be allocated to competing processes. Resource scarcity is a traditional scenario in many system engineering disciplines, where available resources (e.g., machines, robots, drives, etc.) can be shared among concurrently running processes (e.g., components, vehicles, programs, data, etc.), and they must compete to be allocated and achieve some system goal, such as maximizing completion time and minimizing latency. The Petri net is a manufacturing system modeling method appearing in recent years, and is a manufacturing system modeling technology suitable for concurrent, asynchronous, distributed and parallel manufacturing systems. The Petri network has both a strict mathematical expression mode and an intuitive graphic expression mode, has rich system description means and system behavior analysis technology, and provides a solid conceptual foundation for computer science. In the real world, a complete representation of the problem often cannot be accomplished by only a single index, but involves multiple, conflicting and disproportionate objectives, with some difficulty in accurately and efficiently determining these multiple target values to select the optimal solution.
In response to this problem, the extension of the Dijkstra algorithm to multi-objective situations was studied by HANSEN, p.1979. criterion path schemes, in LNEMS 177.Springer, 109-127. LOUI, R.P.1983.optimal pages in graphs with storage and multimedia weights. Comm.ACM26,9(Sept.),670, 676 further analyzes the problem and shows that some random search problems can be reduced to multi-target search problems. Steaward, b.s., AND WHITE, c.c.1991.multiobjective a. j.acm 38,4, 775-. MANDOW, L., AND P' EREZ DE LA CRUZ, J.L.2003. Multicriterion empirical search. Europ.J.Opera.Res.150, 253-280. A systematic extension of a heuristic search paradigm to multi-criteria cases was proposed AND acceptability conditions were analyzed for algorithms with different multi-criteria preference relationships (multi-objective, multi-attribute, lexicographic AND objective-based). However, most of the existing researches are theoretical experiments, and a complete method and a complete program are not realized.
Disclosure of Invention
The invention aims to provide a multi-target scheduling method of a workshop manufacturing system based on a timed Petri network, which is quick, effective and small in operation scale.
The technical solution for realizing the purpose of the invention is as follows: a multi-target scheduling method for a workshop manufacturing system based on a timed Petri network comprises the following steps:
step 1, modeling a multi-target workshop manufacturing system by using a Petri network;
step 2, reading attribute values corresponding to all libraries in the Petri network model established in the step 1, and solving an incidence matrix between the libraries and the transition in the Petri network model;
3, expanding child nodes from the initial node until all target nodes are found based on the incidence matrix and the A-search algorithm in the step 2, and completing system multi-target scheduling; the node is
Wherein m is i Is the number of tokkens in the ith library, p i And (4) representing the ith place, wherein n is the total number of places in the Petri net model.
Compared with the prior art, the invention has the following remarkable advantages: 1) based on the Petri net modeling, the activities, resources and constraints of the system can be expressed concisely, and the scheduling problem of the system can be better described; 2) by adopting the A-search algorithm technology, the size of the structure represented by the system model can be effectively reduced, and the speed of model calculation and analysis can be increased; 3) on the basis of Petri network modeling, a heuristic scheduling method is utilized to further reduce the structural size represented by a system model and accelerate the model calculation and analysis speed, and the system target path acquisition speed is obviously higher than that of the existing non-heuristic algorithm.
Drawings
FIG. 1 is a flow chart of the multi-target scheduling method of the workshop manufacturing system based on the timed Petri network.
Fig. 2 is a schematic diagram of a Petri network model case.
FIG. 3 is a schematic diagram of an example manufacturing system for a plant in an embodiment of the invention.
FIG. 4 is a schematic diagram of a Petri Net model of the plant manufacturing system of FIG. 3.
Detailed Description
With reference to fig. 1, the multi-target scheduling method for the workshop manufacturing system based on the timed Petri network, disclosed by the invention, comprises the following steps of:
step 1, modeling a multi-target workshop manufacturing system by using a Petri network.
And 2, reading attribute values corresponding to all the libraries in the Petri network model established in the step 1, and solving a correlation matrix between the libraries and the transition in the Petri network model.
3, expanding child nodes from the initial node until all target nodes are found based on the incidence matrix and the A-search algorithm in the step 2, and completing system multi-target scheduling; the node is
Wherein m is i In the ith libraryToken number, p i And (4) representing the ith place, wherein n is the total number of places in the Petri net model.
In the Petri network, a transition t represents a certain event, and the event can be represented by adopting the enabling of the transition, wherein the precondition for representing the occurrence of the event is satisfied. The input library of t is adopted to represent the local precondition states required by the occurrence of the event, the input function from the input library to t is adopted to define the number of times the local precondition states are required to be realized, and the realization condition of the local precondition states is represented by the number of the Token contained in the library. Thus, t is enabled not only with respect to its input function, but also with respect to the number of tokens in all of its input bins. The transition enabling conditions are therefore: a transition T ∈ T is enabled under the flag M, if and only if:
the occurrence of an event for which all preconditions are satisfied will "consume" these preconditions while changing the local state associated with the event. In Petri nets, transitions-enabled emissions are often used to describe the occurrence of events. The consumed precondition states and the times thereof are defined by the input function of the transition and expressed by removing a corresponding number of tokens from the input library; the resulting states and their times are determined by the output function and are represented by corresponding tokens added to the output library. The identification of the Petri net changes due to the decrease of the tokens in the input repository and the increase of the tokens in the output repository. To this end, the following transition emission rules were introduced: the transmission of the transition t enabled under the identity M will result in a new identity M':
the algorithm for dynamic operation of the Petri network is as follows:
further, the step 1 of modeling the multi-target workshop manufacturing system by using the Petri network specifically comprises the following steps:
using Petri nets subclass S 3 The PN network models a workshop manufacturing system: the Token number in the library indicates the number of resources, the transition indicates a work component in the shop manufacturing system, and the relationship arc between the library and the transition indicates a rule in the shop manufacturing system. Therefore, the actual workshop manufacturing system is modeled, and the Petri net model can be analyzed and processed to realize the research and control of the actual system.
Wherein S is 3 The PR network is a common Petri network with one arc weight value and formed by compounding a plurality of network structure shared resources, and divides the library into a resource library place, an active library place and an idle library place. The number of tokens in the free pool represents the maximum number of concurrent operations in a production sequence. Resource pools represent resource components in a production sequence, such as robots and production appliances. The active library represents operations that perform processing on parts in a production sequence. In an actual industrial production process, various operations of a production sequence are always performed around resource components, so that in the corresponding Petri network model, the transfer process of the Token in the library is also evolved around the resource library.
Further, the step 2 of solving the incidence matrix between the library and the transition in the Petri net model specifically includes:
step 2-1, initializing an incidence matrix M: each element M of the matrix i,j Denotes a library p i And transition t j Setting all elements in the matrix as 0;
2-2, calculating the association degree between the database and the transition, and updating an association matrix M: when depot p i Token in (1) can directly reach transition t j Then M will be i,j Setting the value as-1; when transition t i Token in (1) can directly reach the depot p j Then M is processed i,j Is set to 1.
With reference to fig. 2 and table 1 below, the program needs to input two files, one is a matrix.txt file storing the transposed correlation matrix of the Petri net model (the correlation matrix of the simple net can be written directly, the correlation matrix of the complex net is generated by INA software, note that the transposed situation, i.e. the row represents the transition, and the column represents the place of the library); and the other is an init. By reading in these two files, the program can learn all the information of the Petri net model.
The first behavior of the init file is an initial identifier of the Petri network, and in an initial state, a plurality of numbers corresponding to the tokens in each library are just a plurality. In fig. 2, there are 21 bins, and there is one token in each of the bins 1, 8, 14, 15, 16, 17, 18, and 19 in the initial state, so the value of these bins is 1, and the value of the rest of bins is 0 without token, and each value is separated by a single space. (Note: there may be more than 1 Token case). The cost required by running each library of the Petri net of the second behavior of the init file is combined with the attributes (3, 1) of p2 of the library No. 2 in the following table 1, and the first attribute cost of 3 and the second attribute cost of 1 are required for running the operation represented by the library. Each operation library corresponds to an operation cost, and if not, the operation cost is (0, 0) (for example, the library representing the buffer or the resource has no operation cost). The third row of the init file is a target identifier of the Petri network, namely the final state required to be reached, the target identifier is the same as the initial identifier, and a plurality of corresponding numbers of the Token in each library in the target state are just a few. In fig. 2, in the target state, each of the libraries 14, 15, 16, 17, 18, 19, 20, and 21 has a dot indicating that there is one tocken, so the value of these libraries is 1, and none of the libraries is 0.
Table 1 attributes of various libraries in the Petri net model shown in fig. 2
The matrix file is used for recording a transposed association matrix of the Petri network, the number of rows is the number of transitions t, and the number of columns is the number of libraries p. There are 14 transitions t1 to t14 in FIG. 2, and 21 libraries p1 to p21, so the matrix file is 14 rows and 21 columns. 1 represents an arc pointed to the library by the transition (namely, the trigger of the transition increases the Token number of the library, so that the Token number is positive), and the arc weight is 1; 1 represents the arc pointed to by the bin for a transition (i.e., the triggering of a transition will reduce the number of false bins, so negative), and the arc weight is 1; 0 indicates that the transition is connected with the library without an arc.
Further, in the step 3, based on the incidence matrix and the a-star search algorithm in the step 2, the child nodes are expanded from the starting node until all the target nodes are found; the method specifically comprises the following steps:
step 3-1, initialize the following list: the OPEN list contains only the start node S 0 The CLOSED list and the SOLUTION list are both empty;
3-2, selecting a first node in the OPEN list as a current node to be expanded, removing the node from the OPEN list and adding the node into the CLOSED list; meanwhile, judging whether the current node to be expanded is a target node, if so, adding the node into a SOLUTION list;
3-3, acquiring all child nodes of the current node to be expanded according to the incidence matrix acquired in the step 2, and acquiring an attribute value of each child node;
3-4, screening all more optimal child nodes from all child nodes according to the attribute values of the child nodes, and adding all more optimal child nodes into an OPEN list;
step 3-5, returning to execute the step 3-2 until the OPEN list is empty;
and 3-6, sequentially outputting all nodes in the SOLUTION list, namely acquiring a path expanded by the nodes and finishing multi-target scheduling.
Further preferably, the attribute value of each child node in step 3-3 includes: g value, H value, F value, triggered transition, node depth, current triggerable transition and node mark;
the H value represents the pre-estimated cost from the current node S to the target node along the optimal path, and the H value is a heuristic function value constructed according to the Petri net model;
the G value and the F value respectively represent the cost from the starting node to the current node S, and the optimal path from the starting node to the target node along the current node S, and the calculation formula is as follows:
F＝G+H。
wherein, the heuristic function needs to be designed according to different Petri nets to obtain the optimal operation efficiency. The present invention proposes a generic and simple heuristic function whose meaning is defined by the non-dominant members of the attribute set to test and compare other more complex and specific functions. For example, the attribute set is (1, 2), (3, 1), (1, 3), one of which is dominated (because 1 ═ 1 and 2<3, (1, 2) is more dominant than (1, 3), (1, 3) by (1, 2)), so the heuristic attributes are (1, 2), (3, 1). This heuristic function is adopted because it is the minimum of all the attributes, obviously satisfying the above conditions.
Another set of more efficient heuristic functions is:
further preferably, in step 3-4, according to the attribute values of the child nodes, all the more optimal child nodes are screened from all the child nodes and added to the OPEN list, specifically:
judging whether a certain child node a exists in the OPEN list and the CLOSED list:
(1) if the OPEN list has the node b which is the same as the child node a, and the attribute value of the child node a is superior to that of the node b, adding the child node a into the OPEN list and deleting the node b;
(2) if the CLOSED list has the node b which is the same as the child node a, and the attribute value of the child node a is superior to that of the node b, adding the child node a into the OPEN list and deleting the node b from the CLOSED list;
(3) if the same node as the child node a does not exist in the OPEN list and the CLOSED list, adding the child node a into the OPEN list;
the specific process of adding the node into the OPEN list is as follows: and according to the attribute values of the nodes, all the nodes in the OPEN list are arranged in an ascending order, so that the nodes to be added are added to the corresponding positions in the OPEN list.
The multi-target system scheduling algorithm for generating the final heuristic Petri network is as follows:
examples
An example of the multi-target shop manufacturing system employed in the present embodiment is shown in fig. 3, and includes three robots (R) 1 ,R 2 ,R 3 : each robot can hold a product at the same time) and four machines (M) 1 ,M 2 ,M 3 ,M 4 : two products per machine can be processed at the same time), and three input buffers (I) 1 ,I 2 ,I 3 ) And three output buffers (O) 1 ,O 2 ,O 3 ). The system is mainly divided into three production lines by taking three robots as a core, and the operation flow of the system is as follows:
J 1 :I 1 →R 1 →M 1 →R 2 →M 2 →R 3 →O 1
or I 1 →R 1 →M 3 →R 2 →M 4 →R 3 →O 1
J 2 :I 2 →R 2 →M 2 →R 2 →O 2
J 3 :I 3 →R 3 →M 4 →R 2 →M 3 →R 1 →O 3
the invention relates to a multi-target scheduling method of a workshop manufacturing system based on a timed Petri network, which comprises the following contents:
1. modeling the plant manufacturing system shown in FIG. 3 by using a Petri Net to obtain a Petri Net model as shown in FIG. 4. Set of all libraries P ═ { P ═ P 1 ,p 2 ,p 3 …p 26 Wherein the resource pool P R ＝{p 20 ,p 21 ,p 22 ,p 23 ,p 24 ,p 25 ,p 26 }, free depot P I ＝{p 1 ,p 5 ,p 14 }, place of activity P A ＝{p 2 ,p 3 ,p 4 ,p 6 ,p 7 ,p 8 ,p 9 ,p 10 ,p 11 ,p 12 ,p 13 ,p 15 ,p 16 ,p 17 ,p 18 ,p 19 }。
2. And (3) reading attribute values corresponding to all the libraries in the Petri network model established in the step (1), and solving a correlation matrix between the libraries and the transition in the Petri network model.
The attribute values obtained in this embodiment are:
the incidence matrix is:
3. based on the incidence matrix and A-search algorithm of the 2, the initial node S 0 And starting to expand the child nodes until all target nodes are found, and finishing the multi-target scheduling of the system.
The start node S in this embodiment 0 Comprises the following steps: s 0 ＝(3p 1 +3p 5 +3p 14 +p 20 +p 21 +p 22 +p 23 +p 24 +p 25 +p 26 )；
In order to highlight the differences of the different heuristic functions, three different heuristic functions, H, are designed 1 ＝(0,0),H 2 ＝(min(hop 1 ),min(hop 2 )),H 3 ＝(max i {ξ i 1 (S)},maxi{ξ i 2 (S)})。H 3 The program of (1)The RST information is as follows:
(0,0)(7,4)(0,0)(0,0)(4,2)(0,0)(0,0)
(0,0)(5,1)(0,0)(0,0)(4,2)(0,0)(0,0)
(0,0)(5,1)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(3,3)(3,1)(4,4)(0,0)(0,0)(0,0)(0,0)
(0,0)(3,1)(4,4)(0,0)(0,0)(0,0)(0,0)
(0,0)(6,1)(4,4)(0,0)(2,1)(0,0)(0,0)
(0,0)(0,0)(4,4)(0,0)(2,1)(0,0)(0,0)
(0,0)(0,0)(4,4)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(3,2)(4,4)(0,0)(0,0)(0,0)(4,5)
(0,0)(0,0)(4,4)(0,0)(0,0)(0,0)(4,5)
(0,0)(0,0)(4,4)(0,0)(0,0)(0,0)(0,0)
(2,3)(4,1)(6,3)(0,0)(0,0)(6,2)(3,4)
(2,3)(4,1)(0,0)(0,0)(0,0)(6,2)(3,4)
(2,3)(4,1)(0,0)(0,0)(0,0)(6,2)(0,0)
(2,3)(0,0)(0,0)(0,0)(0,0)(6,2)(0,0)
(2,3)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)
(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)(0,0)。
4. three different heuristic functions are utilized to respectively carry out a path solving experiment from the starting node to the target node on the Petri network model shown in the figure 4, and the obtained path and the cost required by the path completion are the solution solved by the experiment. Since the use of different heuristic functions does not affect the experimental results, but only the operation speed, the experimental results obtained by using three heuristic functions are the same. The experimental result obtains three non-dominated solutions (55, 37), (58, 30), (59, 27), each solution represents the cost required for completing the path from the starting node to the target node, so the experiment has three different paths which can reach, and the sequential triggering sequence of the transition is a complete path. The three paths are shown in tables 2 to 4, the first column in the table is the name of each transition, the second column is the cost when each transition is triggered, and the transitions are triggered sequentially from left to right and from top to bottom to form one path.
Table 2 first solution (55, 37) of the Petri net model shown in fig. 4
Table 3 second solution (58, 30) of the Petri net model shown in fig. 4
Table 4 third solution of the Petri net model shown in fig. 4 (59, 27)
The invention takes a timed Petri network model of a workshop manufacturing system as an object, adopts a multi-objective heuristic scheduling method, finds out a non-dominated scheduling scheme which best meets the requirement by comprehensively judging a plurality of attributes of the target, can solve all non-dominated solutions aiming at different attributes, and adopts a heuristic multi-objective A algorithm to obtain the system scheduling scheme meeting the requirement without expanding all nodes of the system.
Claims (4)
1. A multi-target scheduling method for a workshop manufacturing system based on a timed Petri network is characterized by comprising the following steps:
step 1, modeling a multi-target workshop manufacturing system by using a Petri network; the method specifically comprises the following steps:
using Petri nets subclass S 3 Modeling a workshop manufacturing system by the PN network: the Token number in the library represents the number of resources, the transition represents a working component in the workshop manufacturing system, and the relation arc between the library and the transition represents a rule in the workshop manufacturing system;
step 2, reading attribute values corresponding to all libraries in the Petri network model established in the step 1, and solving an incidence matrix between the libraries and the transition in the Petri network model; the method for solving the incidence matrix between the place and the transition in the Petri network model specifically comprises the following steps:
step 2-1, initializing an incidence matrix M: each element M of the matrix i,j Denotes a library p i And transition t j Setting all elements in the matrix as 0;
2-2, calculating the association degree between the database and the transition, and updating an association matrix M: when store place p i Token in (1) can directly reach transition t j Then M will be i,j Setting the value as-1; when transition t i Token in (1) can directly reach the depot p j Then M is processed i,j Setting as 1;
3, expanding child nodes from the initial node until all target nodes are found based on the incidence matrix and the A-search algorithm in the step 2, and completing system multi-target scheduling; the node is
Wherein m is i Is the number of tokkens, p, in the ith library i And (4) representing the ith place, wherein n is the total number of places in the Petri net model.
2. The multi-target scheduling method for the timed Petri net-based plant manufacturing system according to claim 1, wherein in the step 3, the incidence matrix and A-search algorithm based on the step 2 are used for expanding the child nodes from the initial node until all the target nodes are found; the method specifically comprises the following steps:
step 3-1, initialize the following list: the OPEN list contains only the start node S 0 The CLOSED list and the SOLUTION list are both empty;
3-2, selecting a first node in the OPEN list as a current node to be expanded, removing the node from the OPEN list and adding the node into the CLOSED list; meanwhile, judging whether the current node to be expanded is a target node or not, if so, adding the node into a SOLUTION list;
3-3, acquiring all child nodes of the current node to be expanded according to the incidence matrix acquired in the step 2, and acquiring an attribute value of each child node;
3-4, screening all more optimal child nodes from all child nodes according to the attribute values of the child nodes, and adding all more optimal child nodes into an OPEN list;
step 3-5, returning to execute the step 3-2 until the OPEN list is empty;
and 3-6, sequentially outputting all nodes in the SOLUTION list, namely acquiring a path expanded by the nodes and finishing multi-target scheduling.
3. The multi-target scheduling method for the timed Petri net-based plant manufacturing system according to claim 2, wherein the attribute value of each child node in the step 3-3 comprises the following steps: g value, H value, F value, triggered transition, node depth, current triggerable transition and node mark;
the H value represents the pre-estimated cost from the current node S to the target node along the optimal path, and the H value is a heuristic function value constructed according to the Petri net model;
the G value and the F value respectively represent the cost from the starting node to the current node S, and the optimal path from the starting node to the target node along the current node S, and the calculation formula is as follows:
F＝G+H。
4. the multi-target scheduling method for the plant manufacturing system based on the timed Petri network as claimed in claim 3, wherein in the step 3-4, all the better child nodes are screened from all the child nodes according to the attribute values of the child nodes, and all the better child nodes are added into an OPEN list, and specifically:
judging whether a certain child node a exists in the OPEN list and the CLOSED list:
(1) if the OPEN list has the node b which is the same as the child node a, and the attribute value of the child node a is superior to that of the node b, adding the child node a into the OPEN list and deleting the node b;
(2) if the CLOSED list has the node b which is the same as the child node a, and the attribute value of the child node a is superior to that of the node b, adding the child node a into the OPEN list and deleting the node b from the CLOSED list;
(3) if the same node as the child node a does not exist in the OPEN list and the CLOSED list, adding the child node a into the OPEN list;
the specific process of adding the node into the OPEN list is as follows: and according to the attribute values of the nodes, all the nodes in the OPEN list are arranged in an ascending order, so that the nodes to be added are added to the corresponding positions in the OPEN list.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910261488.6A CN110046810B (en) | 2019-04-02 | 2019-04-02 | Multi-target scheduling method for workshop manufacturing system based on timed Petri network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910261488.6A CN110046810B (en) | 2019-04-02 | 2019-04-02 | Multi-target scheduling method for workshop manufacturing system based on timed Petri network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110046810A CN110046810A (en) | 2019-07-23 |
| CN110046810B true CN110046810B (en) | 2022-08-16 |
Family
ID=67275795
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910261488.6A Expired - Fee Related CN110046810B (en) | 2019-04-02 | 2019-04-02 | Multi-target scheduling method for workshop manufacturing system based on timed Petri network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110046810B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110928253B (en) * | 2019-11-04 | 2022-05-27 | 南京理工大学 | Dynamic weighting heuristic scheduling method for automatic manufacturing system |
| CN110716522B (en) * | 2019-11-04 | 2022-05-27 | 南京理工大学 | Manufacturing enterprise workshop scheduling optimization method based on arbitrary time A-heuristic search |
| CN111401680B (en) * | 2020-02-06 | 2024-01-30 | 西安电子科技大学 | Collaborative activity construction method for automatic manufacturing system with complex process subnetwork |
| CN111428910B (en) * | 2020-02-28 | 2023-12-05 | 西安电子科技大学 | Vehicle shortest time traffic path processing method based on delay Petri network |
| CN111445186B (en) * | 2020-03-27 | 2021-08-24 | 陕西科技大学 | Petri network theory-based vehicle path optimization method with time window |
| CN111667113B (en) * | 2020-06-04 | 2023-04-25 | 西北工业大学 | Rapid deadlock-free branch delimitation scheduling method for flexible manufacturing system |
| CN112506075B (en) * | 2020-11-20 | 2022-11-22 | 重庆交通大学 | TPZN-based intelligent network automobile system cooperative control method |
| CN113469511B (en) * | 2021-06-18 | 2022-05-13 | 武汉理工大学 | AGV trolley task allocation and charging management method and device |
| CN114511234B (en) * | 2022-02-16 | 2023-05-30 | 陕西科技大学 | Method, system, equipment and readable storage medium for robust configuration of production line resources |
| CN115758788B (en) * | 2022-11-30 | 2023-08-22 | 南通大学 | Processing time calculation method of flexible manufacturing system based on Petri net modeling |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105652833A (en) * | 2015-12-30 | 2016-06-08 | 南京理工大学 | Bi-directional intelligent search-based manufacturing enterprise shop scheduling optimization method |
| CN107092736A (en) * | 2017-04-10 | 2017-08-25 | 南京理工大学 | A kind of PLC Hardware Implementations of Shop Floor Petri net model |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7734492B2 (en) * | 2005-04-26 | 2010-06-08 | Xerox Corporation | Validation and analysis of JDF workflows using colored petri nets |
-
2019
- 2019-04-02 CN CN201910261488.6A patent/CN110046810B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105652833A (en) * | 2015-12-30 | 2016-06-08 | 南京理工大学 | Bi-directional intelligent search-based manufacturing enterprise shop scheduling optimization method |
| CN107092736A (en) * | 2017-04-10 | 2017-08-25 | 南京理工大学 | A kind of PLC Hardware Implementations of Shop Floor Petri net model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110046810A (en) | 2019-07-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110046810B (en) | Multi-target scheduling method for workshop manufacturing system based on timed Petri network | |
| Ruiz et al. | Modeling realistic hybrid flexible flowshop scheduling problems | |
| Qin et al. | An effective hybrid discrete grey wolf optimizer for the casting production scheduling problem with multi-objective and multi-constraint | |
| Naderi et al. | The distributed permutation flowshop scheduling problem | |
| Lindig | Fast concept analysis | |
| Jakobović et al. | Evolving priority scheduling heuristics with genetic programming | |
| Wutthisirisart et al. | A two-phased heuristic for relation-based item location | |
| Cheung et al. | Using genetic algorithms and heuristics for job shop scheduling with sequence-dependent setup times | |
| CN102176223B (en) | Protein complex identification method based on key protein and local adaptation | |
| CN104662564B (en) | Data analysis set-up and program | |
| CN103765381A (en) | Parallel operation on B+ trees | |
| CN104699464A (en) | Dependency mesh based instruction-level parallel scheduling method | |
| JP6418431B2 (en) | Method for efficient one-to-one coupling | |
| Liang et al. | Qos-aware web service selection with internal complementarity | |
| JP2014055037A (en) | Loading operation method, system and computer program | |
| Thuijsman et al. | Computational effort of BDD-based supervisor synthesis of extended finite automata | |
| CN110795469B (en) | Spark-based high-dimensional sequence data similarity query method and system | |
| CN110928253B (en) | Dynamic weighting heuristic scheduling method for automatic manufacturing system | |
| Bartz-Beielstein | Stacked generalization of surrogate models-a practical approach | |
| CN106897199A (en) | A kind of batch job running time prediction method that framework common characteristic is processed based on big data | |
| JP2005135221A (en) | Method and device for joining spreadsheet data and program | |
| CN114691302A (en) | Dynamic cache replacement method and device for big data processing | |
| CN107105052A (en) | Heuristic web service composition method based on figure planning | |
| CN111026776B (en) | Query method and device in relational database | |
| CN110716522B (en) | Manufacturing enterprise workshop scheduling optimization method based on arbitrary time A-heuristic search |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20220816 |