Research on the Method Matching of Manufacturing Service Resources in a Cloud Manufacturing Environment
Authors: 
Yanni Sun, Baohua YuAuthors Info & Claims
Pages 398 - 407
Abstract
In order to quickly and accurately search for manufacturing service resources that meet the demand from the cloud manufacturing(CMfg) platform. In this paper, we first define a multigranularity manufacturing service resource description model. On this basis, a two-stage service resource matching method based on semantic similarity-based hierarchical matching and service quality evaluation is proposed, in which the matching degree of each granularity of manufacturing resource supply and demand is first calculated to generate a candidate set of manufacturing service resources that satisfy the conditions. The service quality evaluation index is then used to match the service providers in the candidate set with service quality. The final set of manufacturing service resources that meet user requirements is obtained, and the effectiveness and feasibility of the method are verified by examples.
References
[1]
Bohu Li, Lin Zhang, Shilong Wang, Fei Tao, Junwei Cao, Xiaodan Jiang, Xiao Song, and Xudong Chai. 2010. Clondmannfactnring anewservice-orientednetworkedmannfactnringmodel. ComputerintegratedManufacturingSystems (2010), 1-7.
[2]
Y. Hu, L. Pan, D. Gu, Z. Wang, H. Liu, and Y. Wang. 2021. Matching of Manufacturing Resources in Cloud Manufacturing Environment. Symmetry (2021).
[3]
Yuan, Minghai, Deng, Chaovalitwongse, and A. W. Manufacturing Resource Modeling for Cloud Manufacturing.
[4]
W. J. Feng, C. Yin, X. B. Li, and L. Li. 2017. A classification matching method for manufacturing resource in cloud manufacturing environment. International Journal of Modeling, Simulation, and Scientific Computing (2017), 1750057.
[5]
Shimiao Zhang, Hongyu Shao, Chen Chen, and Yongliang Chen. 2021. Service matching method for surplus sheet material resource in cloud manufacturing environment. Chinese Journal of Engineering Design (2021), 121-131.
[6]
Peijie Zhuang. 2019. Research on Service Modeling and Matching Method of Machine Tool Resources in Cloud Manufacturing Environment., Chongqing University.
[7]
Ying Cheng, Fei Tao, Lida Xu, and Dongming Zhao. 2018. Advanced manufacturing systems: supply–demand matching of manufacturing resource based on complex networks and Internet of Things. Enterprise Information Systems (2018).
[8]
Y. Lu and X. Xu. 2017. A semantic web-based framework for service composition in a cloud manufacturing environment. J. Manuf. Syst. (2017).
[9]
Ting Yu Lin, Yingying Xiao, Chen Yang, Xiaoliang Liu, and Chi Xing. 2016. Manufacturing Capability Service Modeling, Management and Evaluation for Matching Supply and Demand in Cloud Manufacturing. Springer Singapore (2016).
[10]
Yingfeng Zhang, Geng Zhang, Ting Qu, Yang Liu, and Ray Y. Zhong. 2017. Analytical target cascading for optimal configuration of cloud manufacturing services. J. Clean. Prod. (2017), 330-343.
[11]
Shenquan Huang, Xinjian Gu, Hongming Zhou, and Yarong Chen. 2018. Two-dimensional optimization mechanism and method for on-demand supply of manufacturing cloud service. Comput. Ind. Eng. (2018), 47-59.
[12]
M. Zhang, C. Li, Y. Shang, and C. Li. 2018. Research on resource service matching in cloud manufacturing. Manufacturing Letters (2018), S1130609658.
[13]
Lei Wang, Shunsheng Guo, Xixing Li, Baigang Du, and Wenxiang Xu. 2018. Distributed manufacturing resource selection strategy in cloud manufacturing. The International Journal of Advanced Manufacturing Technology (2018), 3375-3388. DOI 10.1007/s00170-016-9866-8
[14]
Hong Jin, Xifan Yao, and Yong Chen. 2017. Correlation-aware QoS modeling and manufacturing cloud service composition. J. Intell. Manuf. (2017), 1947-1960. DOI 10.1007/s10845-015-1080-2
[15]
Hamed Bouzary and F. Frank Chen. 2020. A classification-based approach for integrated service matching and composition in cloud manufacturing. Robot. Cim.-Int. Manuf. (2020), 101989.
[16]
Fei Tao, Jiangfeng Cheng, Ying Cheng, Shixin Gu, Tianyu Zheng, and Hao Yang. 2017. SDMSim: a manufacturing service supply–demand matching simulator under cloud environment. Robot. Cim.-Int. Manuf. (2017), 34-46.
[17]
Chunyang Yu, Wei Zhang, Xun Xu, Yangjian Ji, and Shiqiang Yu. 2018. Data mining based multi-level aggregate service planning for cloud manufacturing. J. Intell. Manuf. (2018), 1351-1361. DOI 10.1007/s10845-015-1184-8
[18]
J. Liu and Y. Chen. 2019. A personalized clustering-based and reliable trust-aware QoS prediction approach for cloud service recommendation in cloud manufacturing. Knowl.-Based Syst. (2019), 43-56.
[19]
M. Yuan, Z. Zhou, X. Cai, C. Sun, and W. Gu. 2020. Service composition model and method in cloud manufacturing. Robot. Cim.-Int. Manuf. (2020), 101840-101841.
[20]
Yankai Wang, Shilong Wang, Bo Yang, Bo Gao, and Sibao Wang. 2022. An effective adaptive adjustment method for service composition exception handling in cloud manufacturing. J. Intell. Manuf. (2022), 735-751. DOI 10.1007/s10845-020-01652-4
[21]
Yefeng Yang, Bo Yang, Shilong Wang, Feng Liu, Yankai Wang, and Xiao Shu. 2019. A dynamic ant-colony genetic algorithm for cloud service composition optimization. The International Journal of Advanced Manufacturing Technology (2019), 355-368. DOI 10.1007/s00170-018-03215-7
[22]
Yi Que, Wei Zhong, Hailin Chen, Xinan Chen, and Xu Ji. 2018. Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing. The International Journal of Advanced Manufacturing Technology (2018), 4455-4465. DOI 10.1007/s00170-018-1925-x
[23]
Tianyang Li, Ting He, Zhongjie Wang, and Yufeng Zhang. 2020. SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition. J. Intell. Manuf. (2020), 681-702. DOI 10.1007/s10845-019-01472-1
[24]
Hamed Bouzary and F. Frank Chen. 2019. A hybrid grey wolf optimizer algorithm with evolutionary operators for optimal QoS-aware service composition and optimal selection in cloud manufacturing. The International Journal of Advanced Manufacturing Technology (2019), 2771-2784. DOI 10.1007/s00170-018-3028-0
[25]
Changyi Li, Jianhe Guan, Tingting Liu, Ning Ma, and Jun Zhang. 2018. An autonomy-oriented method for service composition and optimal selection in cloud manufacturing. The International Journal of Advanced Manufacturing Technology (2018), 2583-2604. DOI 10.1007/s00170-018-1746-y
[26]
Yunliang Huo, Ji Xiong, Qianbing You, Zhixing Guo, and Hai Xiang. 2021. A personalized method of cloud manufacturing service customization. Int. J. Comput. Integ. M. (2021), 440-454. DOI 10.1080/0951192X.2021.1885064
[27]
Shenquan Huang, Yarong Chen, Hongming Zhou, and Xinjian Gu. 2018. Self-organizing evaluation model and algorithm for manufacturing cloud services driven by user behavior. The International Journal of Advanced Manufacturing Technology (2018), 1549-1565. DOI 10.1007/s00170-018-1651-4
[28]
Yu Feng and Biqing Huang. 2020. Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization. J. Intell. Manuf. (2020), 1649-1660. DOI 10.1007/s10845-018-1409-8
[29]
Baodong Li, Yu Yang, Jiafu Su, Zhichao Liang, and Sheng Wang. 2020. Two-sided matching decision-making model with hesitant fuzzy preference information for configuring cloud manufacturing tasks and resources. J. Intell. Manuf. (2020), 2033-2047. DOI 10.1007/s10845-020-01552-7
[30]
Nasir BILAL. 2014. Implementation of Sobol ’ s Method of Global Sensitivity Analysis to a Compressor Simulation Model. In Proceedings of the International Compressor Engineering Conference (2014)
[31]
Wenfei Luan, Ling Lu, Xin Li, and Chunfeng Ma. 2017. Weight Determination of Sustainable Development Indicators Using a Global Sensitivity Analysis Method. Sustainability-Basel (2017), 303. DOI 10.3390/su9020303
[32]
Yusheng Lou. 2021. Research on corporate credit evaluation under public credit information environment. JOURNAL OF STATISTICS AND INFORMATION (2021), 110-118.
[33]
F. L. Gewers, G. R. Ferreira, Hfd Arruda, F. N. Silva, C. H. Comin, D. R. Amancio, and Ldf Costa. 2021. Principal Component Analysis: A Natural Approach to Data Exploration. ACM Computing Surveys (CSUR) (2021).
Index Terms
- Research on the Method Matching of Manufacturing Service Resources in a Cloud Manufacturing Environment
Recommendations
Research on Service Discovery and Matching Based on Ontology and Service Capabilities in Manufacturing Grid
CSIE '09: Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 05In manufacturing grid (MG), manufacturing service discovery and matching are related to the realization of the goal of manufacturing resource sharing. Because of lacking the support of a powerful semantic model, the methods and effects of service ...
Service composition model and method in cloud manufacturing
Highlights- Service composition is divided into four patterns.
- The quality of service (QoS) ...
AbstractThis study optimizes service composition on the basis of task requirements to solve the problem of multitask corresponding multi-service selection. First, the basic path structure and the implementation steps of cloud manufacturing (...
Cloud manufacturing: a new manufacturing paradigm
Combining with the emerged technologies such as cloud computing, the Internet of things, service-oriented technologies and high performance computing, a new manufacturing paradigm – cloud manufacturing CMfg – for solving the bottlenecks in the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
March 2023
824 pages
ISBN:9781450399029
DOI:10.1145/3594315
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 02 August 2023
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICCAI 2023
ICCAI 2023: 2023 9th International Conference on Computing and Artificial Intelligence
March 17 - 20, 2023
Tianjin, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 17Total Downloads
- Downloads (Last 12 months)8
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format