Cloud manufacturing multi-task scheduling optimization method based on game theory

An optimization method and cloud manufacturing technology, applied in manufacturing computing systems, instruments, computing models, etc., can solve problems such as poor population diversity and premature convergence

Active Publication Date: 2020-02-04
ZHEJIANG UNIV OF FINANCE & ECONOMICS
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of this application is to provide a cloud manufacturing multi-task scheduling optimization method based on game theory. This method proposes a new cloud manufacturing multi-task scheduling game model based on game theory, which can obtain better scheduling solutions and overcome the The basic BBO algorithm has the disadvantages of premature convergence and poor population diversity

Method used

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  • Cloud manufacturing multi-task scheduling optimization method based on game theory
  • Cloud manufacturing multi-task scheduling optimization method based on game theory
  • Cloud manufacturing multi-task scheduling optimization method based on game theory

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Experimental program
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Effect test

Embodiment 1

[0112] Such as figure 2 As shown, the value of each element in the first row of the matrix represents the code of the task. For example, the value of the second element is 1, which means the first task, and the number of times the same element value appears is the code of the subtask, for example, the fourth element The value is also 1, and this is the second occurrence of 1, so this 1 represents the second subtask of the first task; the second row of the matrix represents the code of the service, and the third row represents the code of the service provider. Taking the fourth column {1,3,2} as an example, this means that the second subtask of the first task (ie, t 21 ) Is performed by the third service provided by the second supplier (ie, s 32 ).

[0113] This representation method expresses the three decision variables intuitively and clearly, and the independence and correlation between the decision variables is maintained, which is convenient for subsequent migration operatio...

Embodiment 2

[0132] Such as image 3 As shown, habitat i is the emigration habitat, habitat j is the emigration habitat, and habitat j’ is the emigration habitat improved after the migration operation. image 3 Get the first line of habitat i and habitat j to perform the task sequence migration operation.

[0133] Compare the number of tasks in the corresponding slots of habitat i and j. The slots with the same task number in habitat j remain unchanged, while the slots with different task numbers are changed. The tasks in the four gray slots of habitat j in the figure are The number is the same as the task number in the gray slot corresponding to habitat i, so the task number of the corresponding slot in the improved habitat j'remains unchanged. The other slots are changed as follows: first, randomly select about half of the total number of different task numbers in habitat i (that is, the three diagonal grooves in the figure), and place them in the corresponding slots of improved habitat j'in...

Embodiment 3

[0148] Such as Figure 5 As shown, it is the first row in the habitat to be mutated. Two SIVs in the first row are randomly selected and inserted into the first and second slots of the habitat in turn. Then, the remaining SIVs move their positions backward in turn.

[0149] Such as Image 6 Shown are the second and third rows of the habitat to be mutated. Two SIVs are randomly selected and inserted into the first and second slots of the habitat in turn. Then, the remaining SIVs move their positions backward in turn.

[0150] Performing mutation operations on the task sequence and service sequence separately can further enhance the diversity of the population and help obtain the optimal solution.

[0151] It should be noted that, in order to avoid destroying the habitat with the highest HSI value during each iteration, when calculating the mutation rate of the population, the mutation rate of the optimal solution obtained after the migration operation in step 2.3 is set to zero.

[01...

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Abstract

The invention discloses a cloud manufacturing multi-task scheduling optimization method based on a game theory. The method comprises the steps of establishing a CMMS game model based on time, cost andreliability in combination with the game theory; initializing to obtain habitats, and representing each habitat by adopting a matrix; adopting a fitness function based on Nash equilibrium to calculate the habitat suitability index of each habitat, and determining an initial optimal solution according to the habitat suitability index; selecting a habitat to execute migration operation according tothe immigration rate and the emigration rate, and updating the optimal solution; selecting a corresponding habitat to perform mutation operation according to the mutation rate, and updating the optimal solution; updating the population by adopting an elitist replacement strategy; if the number of iterations is reached, outputting an optimal solution, namely an optimal scheduling scheme; otherwise, continuing iteration. According to the method, a new cloud manufacturing multi-task scheduling game model is provided based on the game theory, a better scheduling scheme can be obtained, and the defects of premature convergence and poor population diversity of a basic BBO algorithm are overcome.

Description

Technical field [0001] This application belongs to the technical field of cloud manufacturing task scheduling, and specifically relates to a cloud manufacturing multi-task scheduling optimization method based on game theory. Background technique [0002] Cloud Manufacturing Multi-task Scheduling (CMMS) refers to the change of time, scheduling available manufacturing services to complete various manufacturing tasks, is a combinatorial optimization problem. In the past ten years, many studies have explored the service composition problem of a single manufacturing task, but it is not suitable for the processing of multi-task scheduling problems. In addition, the cloud manufacturing multi-task scheduling model proposed in some documents is mainly to achieve the optimization of all task combination objectives to achieve better cloud manufacturing platform performance. But in this case, only part of the manufacturing tasks may be completed by reliable services at reasonable cost and t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N20/00G06Q10/04G06Q10/06G06Q50/04
CPCG06N20/00G06Q10/04G06Q10/0631G06Q50/04Y02P90/30
Inventor 张帅肖久红张文宇朱长泰何方
Owner ZHEJIANG UNIV OF FINANCE & ECONOMICS
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