CN111598332A - Workshop double-resource integrated scheduling method and system in intelligent manufacturing environment - Google Patents
Workshop double-resource integrated scheduling method and system in intelligent manufacturing environment Download PDFInfo
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Abstract
The invention discloses a method and a system for integrally scheduling double resources of a workshop in an intelligent manufacturing environment, which comprises the following steps: establishing a topological model of an AGV driving map; establishing a bidirectional single-path guiding system according to the topological model; generating a driving path from a bidirectional single-path guiding system by adopting an improved A path searching algorithm; acquiring a processing workpiece sequence of the machine tool according to an improved NSGA-II algorithm; establishing a path conflict elimination strategy based on an improved time window method; and finishing the dispatching of the AGV according to the driving path, the sequence of the processed workpieces and the path conflict elimination strategy, and realizing the transportation of the workpieces from a warehouse to a machine, from the machine to the machine and from the machine to the warehouse. The double-resource integrated scheduling method in the intelligent manufacturing environment improves the efficiency and accuracy of resource allocation and reduces the production cost of enterprises.
Description
Technical Field
The invention relates to a workshop double-resource integrated scheduling method and system in an intelligent manufacturing environment, and belongs to the technical field of industrial software operation.
Background
The workshop scheduling is an NP-Hard problem, although various solving methods exist in the workshop scheduling, the problems of insufficient solving stability and solving precision, large algorithm calculation amount, easy falling into local optimization and the like exist; the double-resource integrated scheduling is the expansion of NP-Hard problem, the solving process is more complex, along with the continuous improvement of the intellectualization of a workshop and the continuous development of factory unmanned, the AGV is used as the key equipment of the intellectualized production, and is widely applied to the cooperative work of the workshop and a machine tool instead of manual transportation.
For the problem of integrated scheduling of processing machines and an AGV in an intelligent manufacturing workshop environment, at present, the solving effect is not good, so that resource allocation is unreasonable, the production cost of an enterprise is increased, and the enterprise urgently needs an efficient double-resource scheduling strategy.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a workshop double-resource integrated scheduling method and a workshop double-resource integrated scheduling system in an intelligent manufacturing environment, and aims to solve the problem that the double-resource configuration efficiency in the existing workshop environment is not high.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a double-resource integrated scheduling method for a workshop in an intelligent manufacturing environment comprises the following steps:
establishing a topological model of an AGV driving map;
establishing a bidirectional single-path guiding system according to the topological model;
generating a driving path from a bidirectional single-path guiding system by adopting an improved A path searching algorithm;
acquiring a processing workpiece sequence of the machine tool according to an improved NSGA-II algorithm;
establishing a path conflict elimination strategy based on an improved time window method;
and finishing the dispatching of the AGV according to the driving path, the sequence of the processed workpieces and the path conflict elimination strategy.
Further, the method for acquiring the sequence of the processing workpieces comprises the following steps:
randomly generating an initial population of a sequence of processed workpieces according to the working procedures of the workpieces and processing equipment;
calculating the processing time of each processing workpiece sequence according to a greedy decoding algorithm;
calculating the sequencing grade and the crowding degree of each machined workpiece sequence according to the machining time;
selecting a machining workpiece sequence genetic population from the initial population of the machining workpiece sequence according to the sorting grade and the crowding degree;
generating a new population from a workpiece sequence genetic population according to the workpiece procedures and the intersection and random variation of processing equipment;
selecting a parent excellent population from the sequence genetic population of the processing workpiece by adopting an elite retention strategy;
combining the new population and the parent excellent population into a new initial population of the sequence of the processing workpieces, and repeating the process;
repeating the process according to iteration algebra, and outputting an optimal processing workpiece sequence population;
and selecting the optimal processing workpiece sequence from the optimal processing workpiece sequence population according to the sorting grade and the crowding degree.
Further, the path collision resolution policy is:
respectively calculating the transportation task completion time of the AGV of the subsequent task based on a waiting path elimination strategy and a path elimination strategy based on path re-planning;
and selecting a path elimination strategy with the shortest transportation task completion time to schedule the AGV of the subsequent task.
Further, the method further comprises: and if the transport task completion time of the waiting-based path elimination strategy and the path elimination strategy based on the path re-planning are equal, selecting the waiting path elimination strategy.
Further, the search process of the improved a-path search algorithm is as follows;
acquiring a first path node connected with a transportation initial point in a bidirectional single-path guiding system;
calculating an evaluation function value of the AGV traveling path according to the transportation initial point and the first path node;
acquiring a first path node corresponding to the minimum evaluation function value as a new initial transportation point;
repeating the steps until the new initial transportation point is the final transportation point;
and connecting all the initial transport points to form the travel path of the AGV.
Further, the method for calculating the evaluation function value includes:
f(p)＝g(p)+h(p)，
wherein, f (p) represents the distance estimated value from the AGV transportation starting point to the AGV transportation terminal point t through the node p; g (p) represents the actual shortest path length from the AGV transportation origin to node p; h (p) represents the path length estimate from node p to AGV transport destination t.
A dual resource integrated scheduling system for a plant in an intelligent manufacturing environment, the system comprising:
a topological model building module: the method comprises the steps of establishing a topological model of an AGV driving map;
the bidirectional single-path guiding system establishing module comprises: the system is used for establishing a bidirectional single-path guiding system according to the topological model;
a generation module: the system comprises a bidirectional single-path guiding system, a route searching algorithm and a route searching algorithm, wherein the bidirectional single-path guiding system is used for generating a driving path from the bidirectional single-path guiding system;
an acquisition module: the method comprises the steps of obtaining a processing workpiece sequence of the machine tool according to an improved NSGA-II algorithm;
a path conflict elimination strategy establishing module: the method comprises the steps of establishing a path conflict elimination strategy based on an improved time window method;
a scheduling module: and the system is used for finishing the dispatching of the AGV according to the driving path, the processing workpiece sequence and the path conflict elimination strategy.
A double-resource integrated scheduling system of a workshop in an intelligent manufacturing environment comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method described above.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method described above.
Compared with the prior art, the invention has the following beneficial effects:
the method adopts an improved A-path search algorithm to plan the AGVs of the AGVs, establishes a path conflict elimination strategy based on an improved time window method, schedules the AGVs according to a driving path and the path conflict elimination strategy, and can realize effective allocation of workshop resources; the method has the advantages of simple calculation, good solving stability and high solving precision, is applied to workshops, improves the efficiency of resource allocation, and can effectively reduce the production cost.
Drawings
FIG. 1 is a flow chart of a dual resource scheduling policy;
FIG. 2 is a topological map model;
FIG. 3 is a two-way single-path guidance system;
FIG. 4 is a flow chart of an improved A-path search algorithm;
FIG. 5 is a flow chart of the modified NSGA-II algorithm;
FIG. 6 is a sequence of machine processing a workpiece;
FIG. 7 is an electronic map of a workshop;
fig. 8 is a schematic diagram of the integrated scheduling of 6 machines, 6 workpieces and 2 machines.
Detailed Description
The present invention will be described in further detail in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The implementation effect of the double-resource scheduling method in the intelligent manufacturing workshop environment is closely connected with the workshop basic environment, so that the double-resource scheduling method is explained on the basis of the AGV running environment and the path search algorithm.
As shown in fig. 1, a method for integrally scheduling double resources of a workshop in an intelligent manufacturing environment includes the following steps:
establishing a topological model of an AGV driving map; as shown in fig. 2, the nodes represent important points in the workshop, including machine tool positions, intersections, automated warehouses, etc., the connecting lines between the points represent the accessible routes in the actual environment, and the weights of the edges represent the lengths of the routes.
Establishing a bidirectional single-path guiding system of the AGV according to the map topology model; as shown in FIG. 3, the AGV may travel in both directions along a route between two nodes.
Generating a driving path from a bidirectional single-path guiding system by adopting an improved A path searching algorithm;
the search process using the improved a-path search algorithm is shown in fig. 4, and includes the following steps: initializing two tables M and N, wherein the table M is a generated but not evaluated point, the table N is an evaluated node, and the transportation starting point s of the AGV is placed in the table M during initial path search, and the table N is empty. Then the nodes connected to the initial transport point s of the AGV are searched and put into the table M, at which time the evaluation function values need to be calculated. The evaluation function of the improved A algorithm is f (p) ═ g (p) + h (p), wherein f (p) is the distance estimation value from the AGV transportation starting point s to the AGV transportation end point t through the node p; g, (p) the actual shortest path length from the AGV transportation starting point s to the node p; h (p) self-defining heuristic function, i.e. using manhattan distance as heuristic function, i.e. h (p) ═ xp-xt|+|yp-ytL. Calculating an evaluation function value, finding out a minimum value f (j) corresponding node from the evaluation function value, moving the node to a table N, if the minimum value is equal, comparing a plurality of selectable paths at this time with the path formed by two nodes at the previous time, and selecting a path which can enable the AGV to travel linearly; and secondly, judging whether the node j is an AGV transportation terminal point t, if so, finishing the search, otherwise, moving adjacent unsearched nodes of the node j into a table M, simultaneously calculating corresponding evaluation function values, solving the node with the minimum evaluation value in the table M, moving the node into N, and judging whether the corresponding point of the minimum value is the terminal point. The intelligent manufacturing shop is a structured production environment, algorithm search processThe evaluation function values are equal, and the original algorithm randomly selects a corresponding node. The algorithm A is improved, if the evaluation function values are equal, the multiple search paths determined at this time are compared with the paths formed by two nodes at the previous time, and the paths enabling the AGV to travel in a straight line are selected.
Acquiring a processing workpiece sequence of the machine tool according to an improved NSGA-II algorithm;
the method comprises the following steps of obtaining a machining workpiece sequence of the machine tool by utilizing an improved NSGA-II algorithm, namely workpieces to be machined by each machine tool and the sequence, and the detailed steps are as follows: the steps are shown in fig. 5:
1) randomly generating an initial population of a sequence of processed workpieces according to the working procedures of the workpieces and processing equipment;
2) calculating the processing time of each processing workpiece sequence according to a greedy decoding algorithm;
3) calculating the sequencing grade and the crowding degree of each machined workpiece sequence according to the machining time;
4) selecting a machining workpiece sequence genetic population from the initial population of the machining workpiece sequence according to the sorting grade and the crowding degree by the competitive bidding competition selection method;
5) generating a new population from a workpiece sequence genetic population according to the workpiece procedures and the intersection and random variation of processing equipment;
6) selecting a parent excellent population from the sequence genetic population of the processing workpiece by adopting an elite retention strategy;
7) combining the new population and the parent excellent population into a new initial population of the sequence of the processing workpieces, and repeating the process;
8) repeating the process according to iteration algebra, and outputting an optimal processing workpiece sequence population;
9) and selecting the optimal processing workpiece sequence from the optimal processing workpiece sequence population according to the sorting grade and the crowding degree.
Compared with the traditional NSGA-II algorithm, the improved NSGA-II algorithm adopted by the patent is improved in the aspect of population selection, the rejection of new individuals generated in the population in the later stage of the traditional elite retention strategy is considered, the diversity of the population is not facilitated, the algorithm is easy to fall into local optimality, the traditional elite retention strategy is used in the earlier stage of iteration, namely, parents and filial generations are combined to form a total population, the population is sorted according to the rapid non-dominance and the crowding degree, the individuals with high grade and high crowding degree are selected, the convergence of the genetic algorithm can be improved by the strategy, the loss of the optimal solution in the evolution process is avoided, a proportion method is adopted in the later stage, the optimal individuals/filial generations in the population are kept in a certain proportion, the diversity of the population is kept, and the algorithm is. The optimal solution obtained by the algorithm is a chromosome, a machined workpiece sequence of the machine tool is obtained through decoding operation, the machined sequence is shown in fig. 6 according to tasks, and 2, 1-6 in the figure indicate the first step machining time of the workpiece 2 for 6 minutes.
Establishing a path conflict elimination strategy based on an improved time window method;
and establishing a path conflict elimination strategy based on the improved time window method. When a transport task of the AGV is obtained, the starting point, the end point, the starting time, the ending time, the walking path and the key point of the transport task of the AGV are recorded and stored. And if the subsequent AGV task path conflicts with the determined AGV transportation path, the previously determined path is kept unchanged, and the subsequent AGV transportation task is re-planned. Then judging the type of the path conflict, respectively calculating the transportation task completion time based on a waiting path elimination strategy (setting waiting time for the AGV to pass through the section and then pass through the AGV for executing the task) and a path elimination strategy (setting the weight value of the conflict path, namely the distance to be infinite, and then searching the path again by using an improved A search algorithm) for the AGV of the subsequent task, and adopting a short-time consuming strategy. The two strategies consume the same time, and the waiting path elimination strategy is preferentially selected.
And finishing the dispatching of the AGV according to the processing workpiece sequence, the driving path and the path conflict elimination strategy.
The detailed scheduling method is as follows:
(1) the optimal solution determines a processing workpiece sequence of the machine tool, and for the individual processing sequence, the AGV first executes a 'double one' request task, namely, a first work procedure of the workpiece is processed on the machine and the first task of the machine is to process the workpiece. If there are multiple "double one" request tasks, the workpieces that take less time are transported first. The AGVs are now both non-tasking AGVs and are in the same position, so the priority of the AGVs will be randomly formed. After the "double one" task is completed, the "single" requested task is transported, i.e. the first pass of the workpiece is processed on the machine. Eventually causing all of the workpieces to be shipped from the warehouse.
(2) The earliest possible delivery time of all the workpieces in the machining process or in the machine tool buffer area is compared to form the earliest possible delivery time set of the workpieces, the minimum value in the earliest possible delivery time set is obtained, the workpiece corresponding to the minimum value is a task to be transported, and the transport starting point and the transport end point of the task are obtained according to the sequence of the workpieces machined by the machine tool.
(3) Each AGV competes for the task, respectively calculates the time of arriving at the task starting point (each AGV uses an improved A algorithm to calculate the running path from the current position to the task starting point, judges whether the running path conflicts with the existing task path, if so, adopts a task elimination strategy, then each AGV calculates the running path, calculates the running time), if any arrives before the transportation time point, selects the AGV with less time consumption, and records and stores the starting point, the end point, the starting time, the ending time, the running path and the key point of the running process that the AGV arrives at the transportation task starting point. And then according to the starting point, the end point and the starting time of the transportation task, calculating the path and the time of passing the key point by using an improved A-algorithm, judging whether conflict exists or not, if so, adopting a path elimination strategy, and finally, recording and storing the starting point, the end point, the starting time, the ending time, the walking path and the key point of the AGV. And if all the AGVs arrive after the transportation time point during time calculation, selecting the AGV which preferentially arrives at the task point, wherein the method is the same as the above method, and thus the detailed description is omitted.
(4) And (4) repeating the processes (2) to (3) until the last workpiece is transported back to the stereoscopic warehouse.
The invention adopts an AGV competition strategy to calculate the workpieces transported by the AGV, and realizes the transportation of the workpieces from a warehouse to a machine, from the machine to the machine and from the machine to the warehouse. In some links at this stage, the AGV driving path needs to be solved, the calculation is time-consuming, the improved a-search algorithm is used at this time, and if path collision occurs, the path collision elimination strategy is also used.
A dual resource integrated scheduling system for a plant in an intelligent manufacturing environment, the system comprising:
a topological model building module: the method comprises the steps of establishing a topological model of an AGV driving map;
the bidirectional single-path guiding system establishing module comprises: the system is used for establishing a bidirectional single-path guiding system according to the topological model;
a generation module: the system comprises a bidirectional single-path guiding system, a route searching algorithm and a route searching algorithm, wherein the bidirectional single-path guiding system is used for generating a driving path from the bidirectional single-path guiding system;
an acquisition module: the method comprises the steps of obtaining a processing workpiece sequence of the machine tool according to an improved NSGA-II algorithm;
a path conflict elimination strategy establishing module: the method comprises the steps of establishing a path conflict elimination strategy based on an improved time window method;
a scheduling module: and the system is used for finishing the dispatching of the AGV according to the driving path, the processing workpiece sequence and the path conflict elimination strategy.
A double-resource integrated scheduling system of a workshop in an intelligent manufacturing environment comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method described above.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method described above.
Example 1:
when a workpiece is machined, firstly, the AGV takes out a blank from the stereoscopic warehouse and conveys the blank to a corresponding machine tool for machining, after the current process of the machine tool is finished, the AGV conveys the blank to other machine tools for machining, and after all processes of the workpiece are finished, the AGV conveys the blank back to the stereoscopic warehouse. The electronic map of this test is shown in fig. 7, the distance between two machine tools is equal to 15m, the distance between two transverse roads is 10m, the distance between two longitudinally adjacent roads is 10m, and there are 6 machine tools and one stereoscopic warehouse. The machining processes for 6 workpieces and the machining times for the different processes by the respective machines are shown in table 1. The minimum distance between two traveling AGVs on each road section is L which is 2.5m, and the traveling speed of the AGVs is V which is 0.25 m/s. The obtained gantt chart is shown in fig. 8, and the double-resource scheduling strategy is applied to workshop scheduling in an intelligent environment, so that production scheduling in the production process is more reasonable, enterprise cost is reduced, and enterprise benefits are increased.
TABLE 1
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (9)
1. A double-resource integrated scheduling method for a workshop in an intelligent manufacturing environment is characterized by comprising the following steps:
establishing a topological model of an AGV driving map;
establishing a bidirectional single-path guiding system according to the topological model;
generating a driving path from a bidirectional single-path guiding system by adopting an improved A path searching algorithm;
acquiring a processing workpiece sequence of the machine tool according to an improved NSGA-II algorithm;
establishing a path conflict elimination strategy based on an improved time window method;
and finishing the dispatching of the AGV according to the driving path, the sequence of the processed workpieces and the path conflict elimination strategy.
2. The method for double-resource integrated scheduling of the workshop in the intelligent manufacturing environment according to claim 1, wherein the method for obtaining the sequence of the processed workpieces is as follows:
randomly generating an initial population of a sequence of processed workpieces according to the working procedures of the workpieces and processing equipment;
calculating the processing time of each processing workpiece sequence according to a greedy decoding algorithm;
calculating the sequencing grade and the crowding degree of each machined workpiece sequence according to the machining time;
selecting a machining workpiece sequence genetic population from the initial population of the machining workpiece sequence according to the sorting grade and the crowding degree;
generating a new population from a workpiece sequence genetic population according to the workpiece procedures and the intersection and random variation of processing equipment;
selecting a parent excellent population from the sequence genetic population of the processing workpiece by adopting an elite retention strategy;
combining the new population and the parent excellent population into a new initial population of the sequence of the processing workpieces, and repeating the process;
repeating the process according to iteration algebra, and outputting an optimal processing workpiece sequence population;
and selecting the optimal processing workpiece sequence from the optimal processing workpiece sequence population according to the sorting grade and the crowding degree.
3. The method for integrated scheduling of double resources of workshop in intelligent manufacturing environment according to claim 1, wherein the path conflict resolution policy is:
respectively calculating the transportation task completion time of the AGV of the subsequent task based on a waiting path elimination strategy and a path elimination strategy based on path re-planning;
and selecting a path elimination strategy with the shortest transportation task completion time to schedule the AGV of the subsequent task.
4. The method for double-resource integrated scheduling of the workshop in the intelligent manufacturing environment according to claim 3, further comprising: and if the transport task completion time of the waiting-based path elimination strategy and the path elimination strategy based on the path re-planning are equal, selecting the waiting path elimination strategy.
5. The method for double-resource integrated scheduling of workshop in intelligent manufacturing environment according to claim 1, wherein the search process of the improved a path search algorithm is as follows;
acquiring a first path node connected with a transportation initial point in a bidirectional single-path guiding system;
calculating an evaluation function value of the AGV traveling path according to the transportation initial point and the first path node;
acquiring a first path node corresponding to the minimum evaluation function value as a new initial transportation point;
repeating the steps until the new initial transportation point is the final transportation point;
and connecting all the initial transport points to form the travel path of the AGV.
6. The method for scheduling the double resource integration of the workshop in the intelligent manufacturing environment according to claim 5, wherein the method for calculating the evaluation function value comprises the following steps:
f(p)＝g(p)+h(p)，
wherein, f (p) represents the distance estimated value from the AGV transportation starting point to the AGV transportation terminal point t through the node p; g (p) represents the actual shortest path length from the AGV transportation origin to node p; h (p) represents the path length estimate from node p to AGV transport destination t.
7. A double-resource integrated scheduling system for a workshop in an intelligent manufacturing environment is characterized by comprising:
a topological model building module: the method comprises the steps of establishing a topological model of an AGV driving map;
the bidirectional single-path guiding system establishing module comprises: the system is used for establishing a bidirectional single-path guiding system according to the topological model;
a generation module: the system comprises a bidirectional single-path guiding system, a route searching algorithm and a route searching algorithm, wherein the bidirectional single-path guiding system is used for generating a driving path from the bidirectional single-path guiding system;
an acquisition module: the method comprises the steps of obtaining a processing workpiece sequence of the machine tool according to an improved NSGA-II algorithm;
a path conflict elimination strategy establishing module: the method comprises the steps of establishing a path conflict elimination strategy based on an improved time window method;
a scheduling module: and the system is used for finishing the dispatching of the AGV according to the driving path, the processing workpiece sequence and the path conflict elimination strategy.
8. A double-resource integrated scheduling system of a workshop in an intelligent manufacturing environment is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 6.
9. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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| CN112817319A (en) * | 2021-01-08 | 2021-05-18 | 刘连英 | AGV dispatching method and system and computer readable storage medium |
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| CN114063584A (en) * | 2021-11-19 | 2022-02-18 | 江苏科技大学 | Scheduling control method, device and system for integrated processing of ship weight-related parts |
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| CN112215518A (en) * | 2020-10-24 | 2021-01-12 | 陈龙龙 | Cloud computing-combined cosmetic production chain scheduling method and artificial intelligence cloud platform |
| CN112817319A (en) * | 2021-01-08 | 2021-05-18 | 刘连英 | AGV dispatching method and system and computer readable storage medium |
| CN113658295A (en) * | 2021-08-12 | 2021-11-16 | 南方电网数字电网研究院有限公司 | Geographic information-based power grid edge layout mapping method and device |
| CN114063584A (en) * | 2021-11-19 | 2022-02-18 | 江苏科技大学 | Scheduling control method, device and system for integrated processing of ship weight-related parts |
| CN114063584B (en) * | 2021-11-19 | 2024-04-26 | 江苏科技大学 | Scheduling control method, device and system for integrated processing of ship weight closing parts |
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