Abstract
As an application field of service science, cloud manufacturing (CM) has been widely studied in vertical and horizontal services. At present, CM’s vertical service research is limited to the technical research of specific processing and manufacturing and gives less consideration to the background of horizontal integration services. The research on horizontal service judges the effect of the combination only according to quality of service (QoS) attributes. In the actual processing process, the combination’s effect depends not only on QoS but also on the geographical aspects, company, and other attributes. Based on this, we propose a tool combination model based on the task sequence (TCMbTS). The TCMbTS establishes the association ontology model of tool resources and tasks and proposes a mapping method to provide vertical service for CM. Then proposed optimized orientation genetic algorithm (OPOGA) to solve the combination optimization and recommendation problem of TCMbTS and provide horizontal services for CM. Finally, an experiment is performed to verify OPOGA with validity and efficiency.
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Abbreviations
- MT:
-
Manufacturing task
- SMEs:
-
Small- and medium-sized enterprises
- CC:
-
Cloud computing
- MaaS:
-
Manufacturing as a Service
- Iot:
-
Internet of Things
- CM:
-
Cloud manufacturing
- GA:
-
Genetic algorithm
- ct :
-
Cutting tool
- t:
-
Sub-task
- T:
-
Total task
- TRset:
-
TR candidate service set
- OPOGA:
-
Optimized orientation genetic algorithm
- TCMbTS:
-
Tool combination model based on task sequence
- QoS:
-
Quality of service
- \(\psi\) :
-
MR-to-MT threshold
- \(\theta\) :
-
Degree of grey correlation
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Acknowledgements
The authors are grateful to the editor and the anonymous reviewers for their constructive comments. The presented work was supported by the National Natural Science Foundation of China (Grant No. U1610112); Research Project Supported by the Shanxi Scholarship Council of China(Grant No. HGKY2019079 ); and the Graduate Education Innovation Project of Shanxi Province (Grant Nos. 2019BY117 and 2019BY118).
Funding
1. National Natural Science Foundation of China (Grant No. U1610112); 2. Research Project Supported by the Shanxi Scholarship Council of China (Grant No. HGKY2019079 ); 3. The Graduate Education Innovation Project of Shanxi Province (Grant Nos. 2019BY117 and 2019BY118)
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Establishment a service selection and combination model TCMbTS. TCMbTS established the ontology model of the tool and the task, combined with the gray correlation method to realize the mapping between the tool and the task, and finally achieved the purpose of service selection.
In order to solve the recommendation in TCMbTS, we improved the genetic algorithm from the evaluation function, coding method, and cross mutation method to achieve fast and efficient tool combination and recommendation.
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Gao, J., Yan, X., Guo, H. et al. Tool combination model based on task sequence using an optimized orientation genetic algorithm. Evol. Intel. 15, 1619–1635 (2022). https://doi.org/10.1007/s12065-021-00571-4
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DOI: https://doi.org/10.1007/s12065-021-00571-4