Agent-based manufacturing service discovery method for cloud manufacturing

Guo, Liang; Wang, Shilong; Kang, Ling; Cao, Yang

doi:10.1007/s00170-015-7221-0

Cited by 43 publications

(13 citation statements)

References 23 publications

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“…A semantics-based intelligent matching method consisting of a product library and function matching module was proposed. Guo et al (2015) proposed an agent-based manufacturing service discovery method for part-level tasks in cloud manufacturing. An agent-based manufacturing service discovery framework is proposed, which consists of four layers: manufacturing service information input layer, manufacturing service information management layer, manufacturing task information input layer and manufacturing service decision system.…”

Section: Matching Discovery and Selectionmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

221

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For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.

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Section: Matching Discovery and Selectionmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

221

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“…In the proposed data representational model, XML is used to represent the infrastructure services. Guo et al proposed a kind of manufacturing service discovery method based on agent technology in order to solve the manufacturing resources discovery in Cloud manufacturing. Chen et al proposed a WSDL‐based approach to semantically annotate the Cloud services for improved Cloud service discovery.…”

Section: Related Workmentioning

confidence: 99%

“…Syntactic and Semantic Approaches -- 22 Manufacturing Service SQL -- 23 All Semantic Similarity -- 24 IaaS SPARQL -- 25 Software Application XQuery -- 26 Storage Service XQuery -- 27 Web Service Regular Expressions and SQL --…”

Section: Iaasmentioning

confidence: 99%

Enhanced semantic similarity measure based on two‐level retrieval model

Djemaa

Nabli

Amous

2019

Concurrency and Computation

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Summary With the rapid spread of services in the Cloud computing environment, it is difficult for users to find the right service. Therefore, the necessity of a search engine with semantic focused crawler becomes a fundamental requirement. However, the huge size and varied functionalities of Cloud services on the Web, together with the lack of standardized and coherent description of services available, have a great effect for crawlers in order to provide effective Cloud services. To solve these issues, we propose a Cloud service discovery crawler that employs a two‐level semantic similarity measure based on both TF‐IDF and LDA models. Moreover, in order to automatically discover and categorize Cloud services, we present a Cloud Service Ontology (CSOnt) that contains a set of concepts defining Cloud service categories. Experimental results show that the proposed method enhances the performance of the focused crawlers and presents an efficient way to parse the Web and collect Web pages relevant to Cloud services.

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“…For the case analyzed, we intend to use the JADE open source platform, which has an extensive application in the literature [1,55,76,77] and is referred as a simple, compact, graphically friendly [48,78] and easy to use [79] platform. The adoption of this software in each desktop computer used in the workstations will allow a GUI interface to the workers, who will be able to follow in real-time the current performance of system' agents and possible scheduling changes.…”

Section: Technical Details Of Implementationmentioning

confidence: 99%

Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

et al. 2018

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Technological developments along with the emergence of Industry 4.0 allow for new approaches to solve industrial problems, such as the Job-shop Scheduling Problem (JSP). In this sense, embedding Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) is a highly promising approach to handle complex and dynamic JSPs. This paper proposes a data exchange framework in order to deal with the JSP considering the state-of-the-art technology regarding MAS, CPS and industrial standards. The proposed framework has self-configuring features to deal with disturbances in the production line. This is possible through the development of an intelligent system based on the use of agents and the Internet of Things (IoT) to achieve real-time data exchange and decision making in the job-shop. The performance of the proposed framework is tested in a simulation study based on a real industrial case. The results substantiate gains in flexibility, scalability and efficiency through the data exchange between factory layers. Finally, the paper presents insights regarding industrial applications in the Industry 4.0 era in general and in particular with regard to the framework implementation in the analyzed industrial case.

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Agent-based manufacturing service discovery method for cloud manufacturing

Cited by 43 publications

References 23 publications

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Enhanced semantic similarity measure based on two‐level retrieval model

Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

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