Authors:
Philippe Bouché
and
Cecilia Zanni
Affiliation:
LGECO - INSA de Strasbourg, France
Keyword(s):
Knowledge-based systems, Knowledge engineering, Modelling and simulation of production systems, Productivity, Discrete event abstraction, Stochastic approach.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Industrial Applications of Artificial Intelligence
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Verification and Validation of Knowledge-Based Systems
Abstract:
In our increasingly competitive world, today companies are implementing improvement strategies in every department and, in particular, in their manufacturing systems. This paper discusses the use of a global method based on a knowledge-based approach for the development of a software tool for modelling and analysis of production flows. This method will help promote the companies competitiveness by guaranteeing the efficiency of their production lines and, therefore, the quality and traceability of the manufactured products. Different kind of techniques will be used: graphic representation of the production, identification of specific behaviour, and research of correlations among events on the production line. Most of these techniques are based on statistical and probabilistic analyses. To carry on high level analyses, a stochastic approach will be used to identify specific behaviour with the aim of defining action plans, etc...