Work place: CAMPUS IAI, B.P. 2263 Libreville, Gabon
E-mail: jnembe@hotmail.com
Website:
Research Interests: Computational Engineering, Computer systems and computational processes, Data Structures and Algorithms
Biography
Jocelyn NEMBE is born in 1967 in Gabon. He received a doctorate in Applied Mathematics from the University Joseph Fourier, Grenoble - France, since 1996. From 1992-1996, he was student-researcher at LMC (Laboratory of Modeling and Computation) of INPG (Institut National Polytechnique de Grenoble). From 1998 to date, he works as a tenured at African Institute of Computer Science. From 2006 to 2015 he also served as Director of Research and Development at the same Institute. Since 2000, Jocelyn NEMBE also runs a consulting firm specialized in computer engineering and data compression applications. His areas of research and teaching focus on stochastic modeling in industrial environments and digital data compression.
By Pierre Moukeli Mbindzoukou Jocelyn Nembe
DOI: https://doi.org/10.5815/ijieeb.2016.05.07, Pub. Date: 8 Sep. 2016
This work is focused on the stationary behavior of a document processing system. This problem can be handled using workflow models; knowing that the techniques used in workflow modeling heavily rely on constrained Petri nets. When using a document processing system, one wishes to know how the system behaves when a new document enters in order to give precise support to the manager’s decision. This requires a good analysis of the system’s performances. But according to many authors, stochastic models, specifically waiting lines should be used instead of Petri nets at a strategic level in order to lead such analysis. The need to study a new model comes from the fact that we wish to provide tools for a decision maker to lead accurate performance analysis in a document processing system. In this paper, amodel for document management systems in an organization is studied. The model has a static and a dynamic component. The static one is a graph which represents transitions between processing units. The dynamic component is composed of a Markov processes and a network of queues which model the set of waiting-lines at each processing unit. Key performance indicators are defined and studied point-wise and on the average. Formulas are given for some example models.
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