Designing a multi-agent system architecture for managing distributed operations within cloud manufacturing
Tóm tắt
Cloud manufacturing (CM) is a challenging scenario in the fourth stage of industrial production (i.e. Industry 4.0). In this context, the fusion of physical and virtual worlds in cyber-physical production systems transforms manufacturing resources into homogeneous services that can be shared and distributed in collaborative environments. CM systems are characterized by intelligent capability management and manufacturing cloud service-management. An interesting research topic in these areas is the production planning with a decentralized pool of homogeneous resources. The distributed Task Scheduling Problem in CM has been partially tackled in the current literature, but some issues, such as the dynamic task arrival, the downtime of machines, the anomalous tasks identification, have not been addressed. Armed with such a vision, we discuss the design of a multi-agent system for managing and monitoring homogeneous manufacturing services in a CM system based on Additive Manufacturing Technologies.
Tài liệu tham khảo
Li BH, Zhang L, Wang SL, Tao F, Cao JW, Jiang XD, Chai XD (2010) Cloud manufacturing: a new service-oriented networked manufacturing model. Comput Integr Manuf Syst 16:1–7
Wang XV, Xu XW (2013) An interoperable solution for cloud manufacturing. Robot Comput Integr Manuf 29(4):232–247
Xu X (2012) From cloud computing to cloud manufacturing. Robot Comput Integr Manuf 28(1):75–86
Liu Y, Xu X, Zhang L, Wang L, Zhong RY (2017) Workload-based multi-task scheduling in cloud manufacturing. Robot Comput Integr Manuf 45:3–20
Adamson G, Wang L, Holm M, Moore P (2017) Cloud manufacturing—a critical review of recent development and future trends. Int J Comput Integr Manuf 30(4–5):1–34
Zhang Y, Zhang G, Qu T, Liu Y, Zhong RY (2017) Analytical target cascading for optimal configuration of cloud manufacturing services. J Clean Prod 151:330–343
Liu XF, Shahriar MR, Al Sunny SMN, Leu MC, Hu L (2017) Cyber–physical manufacturing cloud: architecture, virtualization, communication, and testbed. J Manuf Syst 43:352–364
He W, Xu LD (2015) A state-of-the-art survey of cloud manufacturing. Int J Comput Integr Manuf 28(3):239–250
Wu DZ, Greer MJ, Rosen DW, Schaefer D (2013) Cloud manufacturing: strategic vision and state-of-the-art. J Manuf Syst 32(4):564–579
Zhou L, Zhang L, Laili Y, Zhao C, Xiao Y (2018) Multi-task scheduling of distributed 3D printing services in cloud manufacturing. Int J Adv Manuf Technol 96(9–12):3003–3017
Wooldridge M (2002) An introduction to multi-agent systems. Wiley, New York
Loia V, Tomasiello S, Vaccaro A (2017) Using fuzzy transform in multi-agent based monitoring of smart grids. Inf Sci 388–389:209–224
D’Aniello G, Loia V, Orciuoli F (2015) A multi-agent fuzzy consensus model in a Situation Awareness framework. Appl Soft Comput J 30:430–440
D’Aniello G, Gaeta A, Gaeta M, Tomasiello S (2018) Self-regulated learning with approximate reasoning and situation awareness. J Ambient Intell Humaniz Comput 9(1):151–164
Karnouskos S, Leitao P (2017) Key contributing factors to the acceptance of agents in industrial environments. IEEE Trans Ind Inf 13(2):696–793
Tomasiello S, Gaeta M, Loia V (2016) Quasi-consensus in second-order multi-agent systems with sampled data through fuzzy transform. J Uncertain Syst 10(4):3–10
Cha H-J et al (2015) Multi-agent system-based microgrid operation strategy for demand response. Energies 8(12):14272–14286
De Falco M, Mastrandrea N, Rarità L, Alalawin AA (2017) Negotiating and sharing capacities of large additive manufacturing networks. ICABML Conf Proc 1:440–466. https://doi.org/10.30585/icabml-cp.v1i1.37
Gaeta M, Loia V, Tomasiello S (2013) A generalized functional network for a classifier-quantifiers scheme in a gas-sensing system. Int J Intell Syst 28(10):988–1009
Tomasiello S (2011) A functional network to predict fresh and hardened properties of self-compacting concretes. Int J Numer Methods Biomed Eng 27(6):840–847