Integrated Service Matching and Composition for Cloud Manufacturing Platforms

dc.contributor.advisorChen, F. Frank
dc.contributor.authorBouzary, Hamed
dc.contributor.committeeMemberXu, Kefeng
dc.contributor.committeeMemberWan, Hung-Da
dc.contributor.committeeMemberAlaeddini, Adel
dc.date.accessioned2024-02-09T19:27:14Z
dc.date.available2024-02-09T19:27:14Z
dc.date.issued2020
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractCloud manufacturing has been recognized as a transformative manufacturing paradigm aiming towards producing highly customized products via sharing distributed manufacturing resources and capabilities. One of the pivotal challenges regarding the practical realization of this idea is the process of matching manufacturing resources with personalized service demands. This problem contains two main aspects: (1) retrieval of functionally similar services to form corresponding service candidate sets, and (2) optimal composition of subtasks according to non-functional quality of service (QoS) indexes. However, almost all the research in the field thus far has focused on tackling each of these dimensions individually which hardly corresponds to actual conditions of cloud manufacturing paradigm. To this end, this dissertation proposes a novel integrated approach that addresses these two problems simultaneously. First, TF-IDF (Term Frequency-Inverse Document Frequency) method coupled with classification algorithms is deployed to retrieve and form the candidate sets. This addresses the oversimplification existing in the literature regarding the predefined service candidate set. Besides, using real-world manufacturing capability data instead of random candidates adds another layer to achieve an even more comprehensive model of the problem. Two novel metaheuristic algorithms were proposed and implemented to effectively solve the service composition and optimal selection problem in the context of cloud manufacturing, especially for large-scale scenarios. The results substantiate the effectiveness of the proposed approaches.
dc.description.departmentMechanical Engineering
dc.format.extent113 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9798607349851
dc.identifier.urihttps://hdl.handle.net/20.500.12588/2844
dc.languageen
dc.subjectCloud manufacturing
dc.subjectTransformative manufacturing
dc.subjectCustomized products
dc.subject.classificationEngineering
dc.subject.classificationMechanical engineering
dc.titleIntegrated Service Matching and Composition for Cloud Manufacturing Platforms
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentMechanical Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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