A flexible multi-task proactive scheduling optimization method in cloud manufacturing environment

An optimization method and multi-task technology, applied in manufacturing computing systems, artificial life, data processing applications, etc., can solve problems such as single goal, weak population diversity, and difficulty in obtaining scheduling solutions, and achieve the effect of overcoming diversity

Active Publication Date: 2022-03-15
ZHEJIANG UNIV OF FINANCE & ECONOMICS
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] However, the existing proactive scheduling methods often consider a single goal, the diversity of the population obtained by the optimization method is weak, and it converges prematurely in the process of optimizing the objective function, making it difficult to obtain a better scheduling scheme

Method used

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  • A flexible multi-task proactive scheduling optimization method in cloud manufacturing environment
  • A flexible multi-task proactive scheduling optimization method in cloud manufacturing environment
  • A flexible multi-task proactive scheduling optimization method in cloud manufacturing environment

Examples

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Effect test

Embodiment 1

[0224] Each task in this experiment contains multiple subtasks, and each subtask can be completed by any available service. Different services used to complete the same subtask have different QoS values. The QoS values ​​are randomly generated within a certain range, but the range of QoS values ​​is limited by the following: time (0-10), cost (0-30) and Reliability (0.99-1).

[0225] The default parameters of the 2S-EGA algorithm are set as follows: (1) The relevant parameters of the GA algorithm: the population size is 50, the maximum number of iterations in the two stages is 500, the algorithm transfers to the second stage after 100 iterations in the first stage, δ 1 =0.8, δ 2 =0.2, δ 3 =0.1; (2) The relevant parameters of the TS algorithm: d=10, the maximum number of iterations of the TS algorithm=150, the length of the tabu list is 30, and the number of taboo candidates is 50; (3) The relevant parameters of the SA algorithm: the initial temperature is 500, The cooling r...

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Abstract

The invention discloses a flexible multi-task proactive scheduling optimization method in the cloud manufacturing environment. The flexible multi-task proactive scheduling optimization method in the cloud manufacturing environment is oriented to the service provider and the service user, and is used to optimize the service provider and the service provider. For task scheduling between users, the flexible multi-task proactive scheduling optimization method in the cloud manufacturing environment includes: establishing a fitness function based on time, cost and reliability; according to the fitness function, establishing a fitness-based, robust The multi-task proactive scheduling objective function of stability and stability; the GA algorithm is used to solve the multi-task proactive scheduling objective function. The invention considers the problem of service interruption, optimizes in combination with multiple objectives, and avoids premature convergence in the optimization process of the objective function while ensuring population diversity, thereby obtaining a more robust and stable scheduling scheme.

Description

technical field [0001] The present application belongs to the field of cloud manufacturing, and specifically relates to a flexible multi-task proactive scheduling optimization method in a cloud manufacturing environment. Background technique [0002] Cloud manufacturing (CMfg) is an emerging service-oriented manufacturing model developed from advanced manufacturing models such as manufacturing grid, agile manufacturing, and virtual manufacturing. Cloud manufacturing combines different types of advanced technologies such as cloud computing, Internet of Things, artificial intelligence, information technology, and service-oriented technology to achieve large-scale sharing and on-demand use of globally distributed manufacturing resources. It is crucial for flexible multi-task scheduling to achieve customized service provision by matching manufacturing services to specific tasks. [0003] The CMfg system can virtualize manufacturing resources and package them into manufacturing ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06Q50/04G06N3/00
CPCG06Q10/06312G06Q10/06315G06Q50/04G06N3/006Y02P90/30
Inventor 张文宇丁捷频王衍张帅熊志英
Owner ZHEJIANG UNIV OF FINANCE & ECONOMICS
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