CN110572282A - Cloud manufacturing service combination optimization method based on k _ Dijkstra algorithm - Google Patents
Cloud manufacturing service combination optimization method based on k _ Dijkstra algorithm Download PDFInfo
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- CN110572282A CN110572282A CN201910797855.4A CN201910797855A CN110572282A CN 110572282 A CN110572282 A CN 110572282A CN 201910797855 A CN201910797855 A CN 201910797855A CN 110572282 A CN110572282 A CN 110572282A
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- H—ELECTRICITY
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H—ELECTRICITY
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- H04L45/00—Routing or path finding of packets in data switching networks
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Abstract
The invention provides a cloud manufacturing service combination optimization method based on a k _ Dijkstra algorithm, which converts a cloud manufacturing service combination optimization problem into a standard directed graph problem, provides a front k shortest path algorithm under a cloud manufacturing mode, namely a k _ Dijkstra algorithm, for the standard directed graph according to a secondary short path theorem, a path expansion method and a Dijkstra algorithm, provides a front k longest path algorithm according to the k shortest path algorithm, and provides front k shortest and longest path algorithms under the cloud manufacturing mode by combining the front k shortest path algorithm and the front k longest path algorithm, so that the optimization of the combined service under the cloud manufacturing mode is realized, and the cloud manufacturing enterprises can be effectively helped to reduce the production cost and improve the production efficiency by optimizing the service time, the service cost, the manufacturing capacity and the comprehensive capacity.
Description
Technical Field
The invention relates to the technical field of cloud manufacturing, in particular to a cloud manufacturing service combination optimization method based on a k _ Dijkstra algorithm.
Background
Cloud manufacturing is a cross fusion product of advanced information technology, manufacturing technology, emerging internet of things technology and the like, is embodied by a manufacturing-as-a-service concept, adopts a current information technology leading-edge concept including cloud computing, and supports the manufacturing industry to provide services with high added value, low cost and global manufacturing for products under a wide network resource environment.
Because various service resources forming cloud manufacturing capability, such as design service, production service, product service and the like, generally embody the characteristics of fuzziness, uncertainty, dynamics and the like, it is difficult to use a unified model for description. The resource cost and the logistics cost of different sub-service parties are different, cloud manufacturing enterprises are difficult to select the optimal service scheme from a plurality of service parties, and cloud manufacturing service combination is lack of optimization, so that the cloud manufacturing enterprises are high in production cost and low in production efficiency.
Disclosure of Invention
In order to solve the problem that cloud manufacturing enterprises are difficult to select the optimal service scheme in the prior art, the invention provides the cloud manufacturing service combination optimization method based on the k _ Dijkstra algorithm, so that the optimal scheme of the cloud manufacturing service is obtained, the production cost is reduced, and the production efficiency is improved.
in order to solve the technical problems, the invention adopts the technical scheme that: the cloud manufacturing service combination optimization method based on the k _ Dijkstra algorithm comprises the following steps:
Converting a service combination scheme directed graph in a cloud manufacturing mode into a standard directed graph;
For the standard directed graph, solving the front k shortest paths and the front k longest paths by using a k _ Dijkstra algorithm;
And judging whether the service combination optimization scheme is the front k shortest path problem or the longest path problem, and solving the optimal solution through a corresponding front k shortest path algorithm or a front k longest path algorithm in the cloud manufacturing mode.
Preferably, the specific operation of converting the service combination scheme directed graph in the cloud manufacturing mode into the standard directed graph is as follows:
For the first n-1 stage subtasks, accumulating the node resource cost to the path from the node of the previous stage to the node, and releasing the node resource cost, namely the resource cost on the path is the sum of the original path resource cost and the node resource cost of the path end point;
for the nth subtask, the resource cost of the node is 0, and the resource cost on the path is the resource cost of the original path.
Preferably, the specific operation of solving the first k shortest paths by using the k _ Dijkstra algorithm is as follows:
Obtaining shortest path S through Dijkstra algorithm0On the basis of shortest path, utilizing secondary short-circuit theorem to obtain second shortest path, on the basis of second shortest path continuously using secondary short-circuit theorem to circularly obtain m-1 times of previous m shortest paths S1、S2…SmThen, the path S obtained by the second shortest path is processed by the path expansion method1、S2…SmExpanding to obtain p expanding paths S11、S12…S1P、S21、S22…S2p…Sm1、Sm2…Smpand finally, sequencing the m × p paths from small to large through a sequencing algorithm, and taking the front k paths as the front k shortest paths.
Preferably, the specific operation of solving the first k longest paths by using the k _ Dijkstra algorithm is as follows:
The original data is inverted, and the shortest path S is obtained through Dijkstra algorithm0On the basis of shortest path, utilizing secondary short-circuit theorem to obtain second shortest path, on the basis of second shortest path continuously using secondary short-circuit theorem to circularly obtain m-1 times of previous m shortest paths S1、S2…Smthen, the path S obtained by the second shortest path is processed by the path expansion method1、S2…SmExpanding to obtain p expanding paths S11、S12…S1P、S21、S22…S2p…Sm1、Sm2…SmpAnd finally, sequencing the m × p paths from small to large through a sequencing algorithm, negating the obtained path data, and taking the first k paths as dataThe first k longest paths.
Preferably, the cloud manufacturing service combination comprises 4 service types of design service, production service, product service and product.
Preferably, the resource cost of all nodes of the standard directed graph is 0, and only the path has the resource cost.
the effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
Compared with the prior art, the invention has the beneficial effects that: according to the method, the optimization problem of the cloud manufacturing service combination is converted into the problem of the standard directed graph, the algorithm of the front k shortest paths under the cloud manufacturing mode is provided for the standard directed graph according to the secondary short path theorem, the path expanding method and the Dijkstra algorithm, namely the k _ Dijkstra algorithm, the algorithm of the front k longest paths is provided, the algorithm of the front k shortest paths and the algorithm of the front k longest paths under the cloud manufacturing mode are provided by combining the algorithm of the front k shortest paths and the algorithm of the front k longest paths, the optimization of the combined service under the cloud manufacturing mode is realized, and the optimization of the service time, the service cost, the manufacturing capability and the comprehensive capability can effectively help cloud manufacturing enterprises to reduce the production cost and improve the production efficiency.
Drawings
FIG. 1 is a directed graph of a preferred embodiment of a cloud manufacturing service portfolio in accordance with the present invention;
FIG. 2 is a cloud manufacturing service portfolio preference schema criteria directed graph of the present invention;
FIG. 3 is a schematic flow chart of the method for solving the k shortest paths by using the k _ Dijkstra algorithm according to the present invention;
FIG. 4 is a schematic flow chart of the method for solving the k longest paths by using the k _ Dijkstra algorithm according to the present invention;
Fig. 5 is a schematic flow chart of solving the k shortest/long paths before the cloud manufacturing mode according to the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "long", "short", etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the drawings, it is only for convenience of description and simplicity of description, but does not indicate or imply that the device or element referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationships in the drawings are only used for illustrative purposes and are not to be construed as limitations of the present patent, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
The technical scheme of the invention is further described in detail by the following specific embodiments in combination with the attached drawings:
Examples
Fig. 1 shows an embodiment of a cloud manufacturing service combination optimization method based on k _ Dijkstra algorithm, which includes the following steps:
Converting a service combination scheme directed graph in a cloud manufacturing mode into a standard directed graph;
For the standard directed graph, solving the front k shortest paths and the front k longest paths by using a k _ Dijkstra algorithm;
and judging whether the service combination optimization scheme is the front k shortest path problem or the longest path problem, and solving the optimal solution through a corresponding front k shortest path algorithm or a front k longest path algorithm in the cloud manufacturing mode.
For the first k minimum/large index service combination schemes of 4 service types of analysis design service, production service, product service and products in the cloud manufacturing mode, the objective function of each service combination scheme is the accumulation of the corresponding index of each subtask service party or the average value of the accumulation, each service combination scheme is the ordered combination of each subtask service party, and the combination process can be represented by a directed graph as shown in fig. 1.
Due to the fact that logistics time and logistics cost exist in the cloud manufacturing service combination process, paths in a directed graph of a service combination scheme may have resource cost, and besides the resource cost of a task and a demand side is 0, other nodes all have resource cost. In the standard directed graph, only the paths have resource cost, and the nodes do not have resource cost, so that the combined and optimized directed graph is converted into the standard directed graph, the cloud manufacturing service combined and optimized problem is converted into the standard directed graph problem, and the shortest and longest paths between two nodes in the standard directed graph are searched.
For the first n-1 stage subtasks, accumulating the node resource cost to the path from the node of the previous stage to the node, and releasing the node resource cost, namely the resource cost on the path is the sum of the original path resource cost and the node resource cost of the path end point;
For the nth subtask, namely the last subtask, since the subtask realizes the logistics transportation from the last subtask server to the demand side, the resource cost of the node is 0, and the resource cost on the path is the resource cost of the original path.
the combined preferred directed graph is converted into the standard directed graph by the method, the resource cost of all nodes is 0, and only the path has the resource cost, as shown in fig. 2.
First, compute the first k shortest paths in cloud manufacturing mode.
Obtaining shortest path S through Dijkstra algorithm0on the basis of shortest path, using secondary short-circuit theorem to obtain second shortest path, on the basis of second shortest path continuously using secondary short-circuit theorem to circularly obtain previous m shortest paths (S) for m-1 times1、S2…Sm) Then, the path (S) obtained by the second shortest path is processed again by the path expansion method1、S2…Sm) Expanding to obtain p expanding paths (S)11、S12…S1P、S21、S22…S2p…Sm1、Sm2…Smp) And finally, sequencing the m × p paths from small to large through a sequencing algorithm, and taking the front k paths as the front k shortest paths of the k _ Dijkstra algorithm.
As shown in fig. 3, the specific operation flow of the first k shortest paths is as follows:
setting the maximum number of paths to mmax，pmax，W'，W'ij,(i+1)jS, initializing path Sm0, p; obtaining the shortest path of the directed graph as the 1 st shortest path through a Dijkstra algorithm; updating Sm(ii) a Calculating and updating the sum All of the path resource costs; making m equal to m + 1; judging whether m is less than or equal to mmaxIf yes, the next shortest path is solved by using the second shortest path theorem, and the updating step is repeated until m is larger than mmax(ii) a When m is greater than mmaxwhen m is 1; solving for S by a path extension methodmExtended path Sm,p(ii) a Judging whether p is less than or equal to pmaxIf not, let p be p +1 and ask S by path expansion methodmExtended path Sm,p(ii) a If so, judging whether m is less than or equal to mmaxWhen m is less than or equal to mmaxIf m is m +1, then find S by path expansion methodmExtended path Sm,pWhen m is greater than mmaxand sequencing the obtained M × P paths from small to large, and taking the first k shortest paths.
then, the first k longest paths in cloud manufacturing mode are computed.
The original data is inverted, and the shortest path S is obtained through Dijkstra algorithm0On the basis of shortest path, using secondary short-circuit theorem to obtain second shortest path, on the basis of second shortest path continuously using secondary short-circuit theorem to circularly obtain previous m shortest paths (S) for m-1 times1、S2…Sm) Then, the path expansion method is used again to obtain the path passing the second shortest pathPath (S)1、S2…Sm) Expanding to obtain p expanding paths (S)11、S12…S1P、S21、S22…S2p…Sm1、Sm2…Smp) And finally, sequencing the m × p paths from small to large through a sequencing algorithm, negating the obtained path data, and taking the front k paths as the front k longest paths of the k _ Dijkstra algorithm.
As shown in fig. 4, the specific operation flow of the first k longest paths is as follows:
Setting the maximum number of paths to mmax，pmax，W'，W'ij,(i+1)jS, initializing path Sm0, p; negating the data of W'; obtaining the shortest path of the directed graph as the 1 st shortest path through a Dijkstra algorithm; updating Sm(ii) a Calculating and updating the sum All of the path resource costs; making m equal to m + 1; judging whether m is less than or equal to mmaxIf yes, the next shortest path is solved by using the second shortest path theorem, and the updating step is repeated until m is larger than mmax(ii) a When m is greater than mmaxwhen m is 1; solving for S by a path extension methodmExtended path Sm,p(ii) a Judging whether p is less than or equal to pmaxif not, let p be p +1 and ask S by path expansion methodmextended path Sm,p(ii) a If so, judging whether m is less than or equal to mmaxWhen m is less than or equal to mmaxIf m is m +1, then find S by path expansion methodmextended path Sm,pWhen m is greater than mmaxsorting the obtained M × P paths from small to large, and taking the first k shortest paths; and negating the obtained data to obtain the first k longest paths.
Because the service optimization problem in the cloud manufacturing mode needs to solve the front k shortest paths and the front k longest paths, the embodiment of the invention combines the front k shortest paths algorithm and the front k longest paths algorithm in the cloud manufacturing mode to provide the front k shortest/long paths algorithm in the cloud manufacturing mode, and the algorithm flow is as follows:
And judging whether the service optimization problem is the front k shortest path problem or the longest path problem, if the service optimization problem is the front k shortest paths problem, solving through a front k shortest path algorithm in the cloud manufacturing mode, otherwise, solving through a front k longest path algorithm in the cloud manufacturing mode, wherein the specific solving flow is shown in fig. 5.
According to the embodiment of the invention, the cloud manufacturing service combination optimization problem is converted into the standard directed graph problem, the front k shortest path algorithm in the cloud manufacturing mode, namely k _ Dijkstra algorithm, is provided for the standard directed graph according to the secondary short path theorem, the path expansion method and the Dijkstra algorithm, the front k longest path algorithm is provided according to the k _ Dijkstra algorithm, the front k shortest and shortest path algorithms in the cloud manufacturing mode are provided by combining the front k shortest path algorithm and the front k longest path algorithm, the optimization of the combined service in the cloud manufacturing mode is realized, and the cloud manufacturing enterprises can be effectively helped to reduce the production cost and improve the production efficiency by optimizing the service time, the service cost, the manufacturing capability and the comprehensive capability.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (6)
1. a cloud manufacturing service combination optimization method based on a k _ Dijkstra algorithm is characterized by comprising the following operations:
Converting a service combination scheme directed graph in a cloud manufacturing mode into a standard directed graph;
For the standard directed graph, solving the front k shortest paths and the front k longest paths by using a k _ Dijkstra algorithm;
And judging whether the service combination optimization scheme is the front k shortest path problem or the longest path problem, and solving the optimal solution through a corresponding front k shortest path algorithm or a front k longest path algorithm in the cloud manufacturing mode.
2. The method for optimizing the cloud manufacturing service composition based on the k _ Dijkstra algorithm according to claim 1, wherein the specific operation of converting the service composition scheme directed graph in the cloud manufacturing mode into the standard directed graph is as follows:
For the first n-1 stage subtasks, accumulating the node resource cost to the path from the node of the previous stage to the node, and releasing the node resource cost, namely the resource cost on the path is the sum of the original path resource cost and the node resource cost of the path end point;
for the nth subtask, the resource cost of the node is 0, and the resource cost on the path is the resource cost of the original path.
3. The method for optimizing the cloud manufacturing service combination based on the k _ Dijkstra algorithm as claimed in claim 1, wherein the operation of solving the first k shortest paths by using the k _ Dijkstra algorithm is as follows:
Obtaining shortest path S through Dijkstra algorithm0On the basis of shortest path, utilizing secondary short-circuit theorem to obtain second shortest path, on the basis of second shortest path continuously using secondary short-circuit theorem to circularly obtain m-1 times of previous m shortest paths S1、S2…SmThen, the path S obtained by the second shortest path is processed by the path expansion method1、S2…SmExpanding to obtain p expanding paths S11、S12…S1P、S21、S22…S2p…Sm1、Sm2…SmpAnd finally, sequencing the m × p paths from small to large through a sequencing algorithm, and taking the front k paths as the front k shortest paths.
4. The method for optimizing the cloud manufacturing service combination based on the k _ Dijkstra algorithm as claimed in claim 1, wherein the operation of solving the k longest paths by using the k _ Dijkstra algorithm is as follows:
The original data is inverted, and the shortest path S is obtained through Dijkstra algorithm0On the basis of shortest path, utilizing secondary short-circuit theorem to obtain second shortest path, on the basis of second shortest path continuously using secondary short-circuit theorem to circularly obtain m-1 times of previous m shortest paths S1、S2…Smthen, the path S obtained by the second shortest path is processed by the path expansion method1、S2…SmExpanding to obtain p expanding paths S11、S12…S1P、S21、S22…S2p…Sm1、Sm2…SmpAnd finally, sequencing the m × p paths from small to large through a sequencing algorithm, negating the obtained path data, and taking the front k paths as the front k longest paths.
5. The method for optimizing the cloud manufacturing service combination based on the k _ Dijkstra algorithm as claimed in any one of claims 1 to 4, wherein the cloud manufacturing service combination comprises 4 service types of design service, production service, product service and product.
6. The method for optimizing the cloud manufacturing service combination based on the k _ Dijkstra algorithm as claimed in any one of claims 1 to 4, wherein the resource cost of all nodes of the standard directed graph is 0, and only the path has the resource cost.
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