Manufacturing task semantic modeling and description in cloud manufacturing system

Wang, Tianri; Guo, Sen; Lee, Chi-Guhn

doi:10.1007/s00170-014-5607-z

Cited by 80 publications

(21 citation statements)

References 27 publications

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“…Formal description of manufacturing resources and capabilities is the base of evaluation, selection, matching, and composition. Wang et al [15] suggested CMfg task semantic modeling and description based on ontology and studied the construction of general CMfg task ontology and the task semantic matching model. How to provide a natural knowledge representation strategy by semantics was discussed in [16], which uses semantic web services to find the matched service resources, and proposed system was applied in a mechanical parts factory.…”

Section: Related Workmentioning

confidence: 99%

Bi-Level Programming Model of Cloud Manufacturing Services Based on Extension Theory

Zhao

Zhou

2018

Mathematical Problems in Engineering

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In order to select the proper cloud manufacturing services to satisfy both sides of supplier and demander, a bi-level programming model is proposed based on extension theory in this paper. Firstly, the cloud model is employed to convert qualitative concepts of language description into quantitative values. Then, according to multicriteria assessment information, the satisfactions of both providers and demanders are calculated by extension evaluation. Finally, respectively taking satisfactions of the demanders and providers as optimization goal of upper layer and lower layer, a mathematical model of bi-level programming is constructed which is solved by linear programming. Compared with traditional recommended methods, the proposed method takes full account of the interests of service providers which have long been neglected. Moreover, extension evaluation is applied to reflect the demand and preference of each subject in detail. Experimental results verify the effectiveness and applicability of the proposed model.

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Section: Related Workmentioning

confidence: 99%

Bi-Level Programming Model of Cloud Manufacturing Services Based on Extension Theory

Zhao

Zhou

2018

Mathematical Problems in Engineering

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“…Fang et al [31] used a hybrid optimization method of resource selection based on grey relation theory and ant colony algorithm for MR optimization configuration. Wang et al [32] proposed a manufacturing task semantic description model for optimal selection in cloud manufacturing system. Leukel et al [33] used a basic idea of Cloud Computing to coordinate the optimal service compositions of service offerings and requests for supply chain.…”

Section: Mrco-nmmentioning

confidence: 99%

Manufacturing resource combinatorial optimization for large complex equipment in group manufacturing: A cluster-based genetic algorithm

Guo¹,

Du²,

Zhao³

et al. 2015

Mechatronics

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“…Cloud manufacturing (CMfg) has been proposed as the panacea to implement this groundbreaking idea in recent years. Nonetheless, to fully achieve this goal, there remains significant issues with regard to enabling technologies [3][4][5][6][7], trust evaluation [8], task and resource description [9,10], scheduling [11][12][13] and service composition, among which the last one, is the key to offering composite services through the above-mentioned collaboration among partners. This has to be done by selecting the optimal resource for each sub-task out of the existing candidates at the resources pool and composing them to achieve the highest quality of service (QoS).…”

Section: Introductionmentioning

confidence: 99%

Optimal composition of tasks in cloud manufacturing platform: a novel hybrid GWO-GA approach

Bouzary¹,

Chen²,

Shahin³

2019

Preprint

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Emerging cloud manufacturing paradigm aims towards providing highly integrated solutions via enabling cooperation between distributed manufacturing resources and capabilities. Implementation of this groundbreaking idea, however, is facing serious challenges springing from the currently centralized industrial structures. In order to help it make its way out, researchers have to address a number of pivotal issues in this regard. Service composition and optimal selection (SCOS), which tackles the problem of optimally selecting and combining available resources into a composite service is one of them. To deal with this NP-hard problem, we developed a novel hybrid algorithm based on the recentlyintroduced grey wolf optimizer (GWO) in which evolutionary operators are also embedded into the hunting mechanism of the basic algorithm.This approach not only makes it possible to adapt an algorithm with continuous structure such as GWO to solve a combinatorial problem such as SCOS, but also empowers it with providing higher exploration through crossover and mutation operators. Experiments conducted clearly proves the superior performance of the proposed algorithm over existing discrete variations of GWO and genetic algorithm, especially in large-scale SCOS problems.

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Manufacturing task semantic modeling and description in cloud manufacturing system

Cited by 80 publications

References 27 publications

Bi-Level Programming Model of Cloud Manufacturing Services Based on Extension Theory

Bi-Level Programming Model of Cloud Manufacturing Services Based on Extension Theory

Manufacturing resource combinatorial optimization for large complex equipment in group manufacturing: A cluster-based genetic algorithm

Optimal composition of tasks in cloud manufacturing platform: a novel hybrid GWO-GA approach

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