Manufacturing Service Management in Cloud Manufacturing: Overview and Future Research Directions

Tao, Fei; Zhang, Linlin; Liu, Yongkui; Cheng, Ying; Wang, Lihui; Xu, Xun

doi:10.1115/1.4030510

Cited by 160 publications

(66 citation statements)

References 59 publications

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“…In the narrow sense, scheduling refers only to the process of allocating resources/services to tasks (or dispatching tasks to resources/services), monitoring, controlling and optimising resource/service status and task execution so as to satisfy consumers' individualised requirements. In the broad sense, scheduling encompasses not only the scheduling process in the narrow sense, but also many other activities such as task processing (especially task decomposition), service discovery, matching, selection and composition that are either involved in or indispensable for the scheduling process (Tao et al 2015;Cheng et al 2017). Thus far, scheduling in the broad sense is a research topic that has attracted most attention in cloud manufacturing , and approximately 158 papers on this topic have been published, amongst which dozens of papers focused on scheduling in the narrow sense.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Wang

et al. 2018

International Journal of Production Research

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203

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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: Introductionmentioning

confidence: 99%

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

Liu

Wang

et al. 2018

International Journal of Production Research

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203

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“…As a promising new paradigm, CMfg has drawn extensive attentions from industrial community and academia [26][27][28][29]. Notable efforts have been expended on the investigation of the following: (1) concept and architecture [30], (2) enabling technologies [31][32][33][34], and (3) application prototype platform [35][36][37].…”

Section: Related Workmentioning

confidence: 99%

QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups

Liu

Zhou

2016

Int J Adv Manuf Technol

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Cloud manufacturing (CMfg) has drawn extensive attentions from industrial community and academia. Quality of service (QoS)-aware service composition is critical to the on-demand use of distributed manufacturing resources and capabilities in CMfg systems. However, most previous work plainly composed composite services by the approach of oneto-one mapping-based service composition (OOM-SC), which leads to drawbacks to both the overall QoS of composite services and the success rate of service composition. To circumvent this, an approach of synergistic elementary service group-based service composition (SESG-SC) is proposed in this paper. It releases the assumption of one-to-one mapping between elementary services and subtasks, allowing a free combination of multiple functionally equivalent elementary services into a synergistic elementary service group (SESG) to perform each subtask collectively, thereby bettering the overall QoS and achieving more acceptable success rate. To introduce an optimal construction of SESGs into the optimization model of QoS-aware service composition, three kinds of redundant structures within SESGs are discussed and the corresponding QoS evaluation formulas are also proposed. To deal with the increasing computing complexity of the optimization model, an algorithm named matrix-coded genetic algorithm with collaboratively evolutional populations (MCGA-CEP) is designed in the current study. The experimental results indicate that the proposed SESG-SC approach significantly outperforms the previous approaches, and the proposed MCGA-CEP is sound on performance-wise.

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“…A parallel method for service composition optimal-selection in cloud manufacturing system was studied by Tao et al [18]. In addition, another researches about cloud manufacturing, cloud computing, and internet of things (IoT) can be seen in references [19][20][21][22][23][24][25][26][27][28][29].…”

Section: Modeling and Topology Of Scnmentioning

confidence: 99%

A new approach for data processing in supply chain network based on FPGA

Zou

Tao

Jiang

et al. 2015

Int J Adv Manuf Technol

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With the development of the supply chain network (SCN) and big data processing, a simple view at above two technologies separately has become unadvisable; this mainly reflected on the growing amount of data in the SCN nodes. Since the data processing is not timely, the potential risks may spread like dominoes to the entire network. There are many small nodes in the SCN, in which it requires data processing equipment that have characteristics of high integration and miniaturization. For the phenomenon that large shape and real-time shortage exists in designing underlying data processing equipment in SCN with the traditional special large computers and general embedded processor, this paper considering the advantage of parallel character of FPGA (field programmable gate array), a new method, which uses FPGA to design embedded device to execute related algorithms in parallel for underling data processing to reduce risks which caused by time-delay in SCN, is proposed. To verify effectiveness of the proposed method, a FPGAbased design method for Kalman filter algorithm and median filter algorithm is proposed. The performance and advantage of the proposed method are analyzed by comparing with traditional methods. The results shown that the proposed method have high real-time and accuracy in complex algorithm execution for SCN underling data processing, which can reduce the potential risk and improve the performance of entire SCN.

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Manufacturing Service Management in Cloud Manufacturing: Overview and Future Research Directions

Cited by 160 publications

References 59 publications

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

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

QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups

A new approach for data processing in supply chain network based on FPGA

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