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    Scalability Planning for Cloud Based Manufacturing Systems

    Source: Journal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 004::page 40911
    Author:
    Wu, Dazhong
    ,
    Rosen, David W.
    ,
    Schaefer, Dirk
    DOI: 10.1115/1.4030266
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Cloudbased manufacturing (CBM) has recently been proposed as an emerging manufacturing paradigm that may potentially change the way manufacturing services are provided and accessed. In the context of CBM, companies may opt to crowdsource part of their manufacturing tasks that are beyond their existing inhouse manufacturing capacity to thirdparty CBM service providers by renting their manufacturing equipment instead of purchasing additional machines. To plan manufacturing scalability for CBM systems, it is crucial to identify potential manufacturing bottlenecks where the entire manufacturing system capacity is limited. Because of the complexity of manufacturing resource sharing behaviors, it is challenging to model and analyze the material flow of CBM systems in which sequential, concurrent, conflicting, cyclic, and mutually exclusive manufacturing processes typically occur. To address and further study this issue, we develop a stochastic Petri nets (SPNs) model to formally represent a CBM system, model and analyze the uncertainties in the complex material flow of the CBM system, evaluate manufacturing performance, and plan manufacturing scalability. We validate this approach by means of a delivery drone example that is used to demonstrate how manufacturers can indeed achieve rapid and costeffective manufacturing scalability in practice by combining inhouse manufacturing and crowdsourcing in a CBM setting.
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      Scalability Planning for Cloud Based Manufacturing Systems

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    http://yetl.yabesh.ir/yetl1/handle/yetl/158706
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    contributor authorWu, Dazhong
    contributor authorRosen, David W.
    contributor authorSchaefer, Dirk
    date accessioned2017-05-09T01:20:26Z
    date available2017-05-09T01:20:26Z
    date issued2015
    identifier issn1087-1357
    identifier othermanu_137_04_040911.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/158706
    description abstractCloudbased manufacturing (CBM) has recently been proposed as an emerging manufacturing paradigm that may potentially change the way manufacturing services are provided and accessed. In the context of CBM, companies may opt to crowdsource part of their manufacturing tasks that are beyond their existing inhouse manufacturing capacity to thirdparty CBM service providers by renting their manufacturing equipment instead of purchasing additional machines. To plan manufacturing scalability for CBM systems, it is crucial to identify potential manufacturing bottlenecks where the entire manufacturing system capacity is limited. Because of the complexity of manufacturing resource sharing behaviors, it is challenging to model and analyze the material flow of CBM systems in which sequential, concurrent, conflicting, cyclic, and mutually exclusive manufacturing processes typically occur. To address and further study this issue, we develop a stochastic Petri nets (SPNs) model to formally represent a CBM system, model and analyze the uncertainties in the complex material flow of the CBM system, evaluate manufacturing performance, and plan manufacturing scalability. We validate this approach by means of a delivery drone example that is used to demonstrate how manufacturers can indeed achieve rapid and costeffective manufacturing scalability in practice by combining inhouse manufacturing and crowdsourcing in a CBM setting.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleScalability Planning for Cloud Based Manufacturing Systems
    typeJournal Paper
    journal volume137
    journal issue4
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4030266
    journal fristpage40911
    journal lastpage40911
    identifier eissn1528-8935
    treeJournal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian