Multitask Oriented Virtual Resource Integration and Optimal Scheduling in Cloud Manufacturing

Cheng, Zhen; Zhan, Dechen; Zhao, Xibin; Wan, Hai

doi:10.1155/2014/369350

Cited by 36 publications

(15 citation statements)

References 12 publications

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“…Cheng et al (2014) studied multi-task-oriented scheduling in cloud manufacturing taking virtual resource correlations into account. The tasks they considered have completely identical subtask execution flows and thus require the same candidate resource sets.…”

Section: Service and Task Schedulingmentioning

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: Service and Task Schedulingmentioning

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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“…DF i,j , CS i,j , BS i,j 0.8540 C 10 DF i,j , CS i,j , QU i,j 0.7149 C 11 DF i,j , BS i,j , ME i,j 0.4255 C 12 NP i,j , MS i,j , SD i,j 0.9206 C 13 NP i,j , MS i,j , TC i,j 0.8757 C 14 NP i,j , MA i,j 0.4294 C 15 NP i,j , CS i,j , TC i,j 0.8521 C 16 NP i,j , ME i,j 0.8038 C 17 MS i,j , SD i,j , RU i,j 0.9946 C 18 MS i,j , RS i,j , TC i,j 0.1193 C 19 MS i,j , QU i,j 0.8209 C 20 MS i,j , TC i,j , RU i,j 0.5309 C 21 MA i,j , PE i,j 0.9671 C 22 MA i,j , RU i,j 0.6952 C 23 OD i,j , RU i,j 0.2307 C 24 SD i,j , TE i,j 0.7174 C 25 BS i,j , ME i,j , TE i,j 0.6907 C 26 RS i,j , ME i,j , TE i,j 0.8250 C 27 ME i,j , PE i,j 0.9906 …”

Section: Cliquementioning

confidence: 99%

“…They only considered facility type to match each request to a set of enterprises, and the facility type reflects the resource type the facility retains. Considering the resource type each enterprise retains, Cheng et al [22] tackled a scheduling problem in CM where enterprises share their resources with each other.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Graphical Framework for Estimating Collaboration Levels in Cloud Manufacturing

2017

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Cloud manufacturing (CM) is an emerging manufacturing model based on collaboration among manufacturing enterprises in a cloud computing environment. Naturally, collaboration is one of main factors that impacts performance in a variety of ways such as quality, lead time, and cost. Therefore, collaboration levels should be considered when solving operational issues in CM. However, there has been no attempt to estimate these levels between enterprises participating in CM. The collaboration level among enterprises in CM is defined as the ability to produce a manufacturing service that satisfies a customer by means of collaborative production amongst enterprises. We measure it as the conditional probability that collaborative performances are high given collaborative performance factors (e.g., resource sharing, information sharing, etc.). In this paper, we propose a framework for estimating collaboration levels. We adopt a probabilistic graphical model (PGM) to develop the framework, since the framework includes a lot of random variables and complex dependencies among them. The framework yields conditional probabilities that two enterprises will reduce the total cost, improve resource utilization or quality through collaboration between them given each enterprise's features, collaboration possibility, and collaboration activities. The collaboration levels the proposed framework yields will help to handle diverse operational problems in CM.

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“…Cheng et al [42] studied multitask oriented virtual resource integration and optimal scheduling in CMfg based on the consideration of sharing and correlation among virtual resources. Tao et al [43] developed a parallel intelligent algorithm for service composition optimal-selection in the CMfg system, in which numerous criteria, especially service-oriented criteria such as reliability, maintainability, trust, and function similarity, are taken into consideration.…”

Section: Literature Reviewmentioning

confidence: 99%

A TQCS-based service selection and scheduling strategy in cloud manufacturing

Cao

Wang

Kang

et al. 2015

Int J Adv Manuf Technol

153

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Globalization, servitization, and customization in the marketplace are changing the way manufacturing enterprises do their business. Cloud manufacturing (CMfg) offers a possibility to perform large-scale manufacturing collaboration. However, CMfg systems are immature in many aspects. Service selection and scheduling is a key issue for practical implementation of CMfg. In this paper, a service selection and scheduling model is established, with criteria TQCS (time, quality, cost, and service) being considered. A fuzzy decision-making theory is adopted to transform TQCS values into relative superiority degrees. This is different from the traditional linear weighted method in most previous research, which results in large values of non-standardization error. The four relative superiority degrees are then combined linearly into an overall objective, in which the weight coefficients are calculated through analytic hierarchy process (AHP). Afterwards, ant colony optimization (ACO) is repurposed for the established service selection and scheduling model. Meanwhile, a selection mechanism is added to ACO (now ACOS) to enhance its validity. The simulation results demonstrate the practicality of the proposed model and the effectiveness of ACOS compared with other widely used algorithms.

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Multitask Oriented Virtual Resource Integration and Optimal Scheduling in Cloud Manufacturing

Cited by 36 publications

References 12 publications

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

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

Probabilistic Graphical Framework for Estimating Collaboration Levels in Cloud Manufacturing

A TQCS-based service selection and scheduling strategy in cloud manufacturing

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