Jocelyn Nembe

Work place: CAMPUS IAI, B.P. 2263 Libreville, Gabon



Research Interests: Computational Engineering, Computer systems and computational processes, Data Structures and Algorithms


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.

Author Articles
A Stochastic Model for Document Processing Systems

By Pierre Moukeli Mbindzoukou Jocelyn Nembe

DOI:, 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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