Multi-centric management and optimized allocation of manufacturing resource and capability in cloud manufacturing system

Lin, Ting; Yang, Chen; Zhuang, Changhui; Xiao, Yingying; Tao, Fei; Shi, Guoqiang; Geng, Chao

doi:10.1177/0954405415624364

Cited by 21 publications

(12 citation statements)

References 22 publications

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“…Information and knowledge are the key resources for decision making [94]. As a result, utilization of these resources results in competency and enhanced decision making-ultimately leading to a competitive advantage [95]. The key to information and knowledge is data [96].…”

Section: Resource-based Viewmentioning

confidence: 99%

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Munodawafa

Johl

2019

Sustainability

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Increased greenhouse gas (GHG) emissions in the past decades have created concerns about the environment. To stymie global warming and the deterioration of the natural environment, global CO2 emissions need to reach approximately 1.3 tons per capita by 2050. However, in Malaysia, CO2 output per capita—driven by fossil fuel consumption and energy production—is expected to reach approximately 12.1 tons by the year 2020. GHG mitigation strategies are needed to address these challenges. Cleaner production, through eco-innovation, has the potential to arrest CO2 emissions and buttress sustainable development. However, the cleaner production process has been hampered by lack of complete data to support decision making. Therefore, using the resource-based view, a preliminary study consisting of energy and utility firms is undertaken to understand the impact of big data analytics towards eco-innovation. Linear regression through SPSS Version 24 reveals that big data analytics could become a strong predictor of eco-innovation. This paper concludes that information and data are key inputs, and big data technology provides firms the opportunity to obtain information, which could influence its production process—and possibly help arrest increasing CO2 emissions.

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Section: Resource-based Viewmentioning

confidence: 99%

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Munodawafa

Johl

2019

Sustainability

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“…Thus, cyber-security issues are critical and deserve more research attention, such as encryption-based partial sharing for collaboration 36,37 and authentication-authorization-accounting (3A). Except for that, issues about manufacturing resource management 38 and scalability of social interactions in the CPSS platform, together with the big data analytics for social and industrial data mining and data-driven intelligent automation 39 should be further discussed.…”

Section: Discussionmentioning

confidence: 99%

Incorporating social sensors, cyber-physical system nodes, and smart products for personalized production in a social manufacturing environment

Ding

Jiang

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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As manufacturing industry heads toward the socialization and mass individualization, customer requirements have become personalized and dynamic. Socialized manufacturing resources spring up in different finer-grained market segments to provide various manufacturing services for customers. To facilitate the mass business collaboration, these resources and customers need to be cyber-physical-social interconnected. This article proposes a cyber-physical-social system for the personalized product production in a social manufacturing environment, which incorporates social sensors in the human end, cyber-physical system nodes in the machine end, and smart products in the product end for social interaction and distributed production control. The three-layer framework of cyber-physical-social system and threestage interaction scenarios are discussed. The multi-role distributed production control mechanism is studied to enhance the agility, responsiveness, flexibility, and coordination capability of the cyber-physical-social system-enabled personalized product production system. Cyber-physical-social system leverages the global cyber-physical-social convergence and the local regional autonomy for the personalized product production. It is expected that this article will contribute to the research areas of industry 4.0-based manufacturing mode innovation and intelligent production process control.

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“…11 CMC was a combination of service resources involving a lot of inter-enterprise collaboration elements, such as transportation cost, time, resource utilization and quality. Lin et al 12 proposed a global optimization model for the manufacturing resources and capability allocation under the multi-centric architecture. As for the CMC, Cheng et al 13 proposed a multi-objective optimization model considering the cost and production load with time-sequence constraints.…”

Section: Brief Reviewmentioning

confidence: 99%

An improved firefly algorithm for collaborative manufacturing chain optimization problem

Zhang

Jing

Zhu

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part B:

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In order to conduct resource sharing and deployment in cloud manufacturing environment, a concept of collaborative manufacturing chain was proposed. Based on machining tasks with the sequential characteristics, the proposed model considering the criteria of service cost, service time, service quality and service utilization was constructed. Fuzzy analytical hierarchy process was adopted to add the above multi-criteria model to a single objective problem. Then, an improved firefly algorithm was used to solve a reasonable collaborative manufacturing chain scheme. Based on the discrete characteristics of the collaborative manufacturing chain, iterative position function was improved to make the solution space to be a discrete domain. Furthermore, particle swarm optimization was used to optimize the step length factor α, attraction degree β0 and light absorption coefficient γ so as to prevent the firefly algorithm from local optimum. Compared with the genetic algorithm, numerical result suggests that the improved firefly algorithm has more advantages in convergence speed and solving efficiency. It is expected that this study can provide a useful reference for the service composition of collaborative manufacturing chain.

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Multi-centric management and optimized allocation of manufacturing resource and capability in cloud manufacturing system

Cited by 21 publications

References 22 publications

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Incorporating social sensors, cyber-physical system nodes, and smart products for personalized production in a social manufacturing environment

An improved firefly algorithm for collaborative manufacturing chain optimization problem

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