Multi-task service composition model considering competitive resource constraint in cloud manufacturing

Service composition optimization is one of the core issues of cloud manufacturing research. At present, relevant researches mostly focus on single-task service composition, and there are few reports on the problem of multi-task service composition considering resource competition. However, in the re...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-04, Vol.1873 (1), p.12083
Main Authors: Zhongning, Wang, Yankai, Wang, Ronghua, Chen, Hui, Huang, Bin, Luo
Format: Article
Language:English
Subjects:
Business competition
Composition
Conflicts of interest
Constraint modelling
Global optimization
Manufacturing
Model testing
Optimization
Physics
Search algorithms
Swarm intelligence
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Summary:Service composition optimization is one of the core issues of cloud manufacturing research. At present, relevant researches mostly focus on single-task service composition, and there are few reports on the problem of multi-task service composition considering resource competition. However, in the real cloud manufacturing environment, multi-task service composition is common, so how to formulate a robust multi-task service composition model is a meaningful research direction. This paper first proposes a multi-task service composition optimization model considering resource competition constraints (MTSCOM-RCC) in the cloud manufacturing environment to fill the above-mentioned gap. The MTSCOM-RCC model considers the conflicts of interest and time between parallel subtasks and proposes an important service evaluation indicator. Secondly, a two-layer coding method, including task layer and subtask, is proposed based on studying the MTSCOM-RCC’s competition mechanism and characteristics. An improved hybrid genetic artificial bee colony algorithm model is proposed to solve the multi-task model. The model is combined with the global optimization method and task queue optimization method. Experimental results prove that the two-layer task optimization method model is more feasible and effective than the other two solution models.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1873/1/012083