Performance-Based Bayesian Learning for Resource Collaboration Optimization in Manufacturing Grid

Authors:
Jian Zhou;Qing Li;Jim Browne;Qing Wang;Paul Folan;Tianyuan Xiao
Affiliations:
Department of Automation, Tsinghua University, Beijing, 100084, P.R. China and National University of Defense Technology, Changsha, 410073, P.R. China;Department of Automation, Tsinghua University, Beijing, 100084, P.R. China;Computer Integrated Manufacturing Research Unit, National University of Ireland, Galway, Ireland;Department of Automation, Tsinghua University, Beijing, 100084, P.R. China;Computer Integrated Manufacturing Research Unit, National University of Ireland, Galway, Ireland;Department of Automation, Tsinghua University, Beijing, 100084, P.R. China
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Year:
2007

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Abstract

Following the rapid development of Grid computing, Grid technology has been introduced into the manufacturing realm and is contemporarily being considered for the sharing of manufacturing resources. However current research in the subject-area is still immature and mainly focuses on conceptual framework development. Here a concrete performance-based Bayesian method for resource collaboration optimization in Extended Enterprise is proposed which improves and promotes research in applying Grid-thinking in inter-organizational manufacturing value chains. Based on the research background, problem statement, and the consideration of Bayesian learning, the method for probability dependency relationship modeling between the performance values of different manufacturing resource nodes in the Extended Enterprise is analysed; and is subsequently complimented by the development of an extended method for more general use. Finally, a system dynamics simulation model for the proposed method is established and the validity and effectivity of the suggested method is tested via a simple case study.