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Towards a Reconfiguration Framework for Systems Engineering Integrating Use Phase Data

Part of: Knowledge-based Engineering

Published online by Cambridge University Press:  26 July 2019

Lara Qasim ,
Andreas Makoto Hein ,
Marija Jankovic ,
Sorin Olaru  and
Jean-Luc Garnier
Lara Qasim*
Affiliation:
CentraleSupelec, Laboratoire Génie Industriel, France; Thales Technical Directorate, France
Andreas Makoto Hein
Affiliation:
CentraleSupelec, Laboratoire Génie Industriel, France;
Marija Jankovic
Affiliation:
CentraleSupelec, Laboratoire Génie Industriel, France;
Sorin Olaru
Affiliation:
CentraleSupelec, Laboratoire de Signaux et Systemes, France;
Jean-Luc Garnier
Affiliation:
Thales Technical Directorate, France
*
Contact: Qasim, Lara, École Centrale Paris, Laboratoire Génie Industriel, France, lara.qasim@centralesupelec.fr

Abstract

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One of the aims of systems engineering is to develop systems with a number of pre-defined configurations, in order to operate effectively and efficiently in different contexts and environments. Early in the design phase, system reconfiguration allows to propose and optimize these configurations. With regard to the literature review and industrial observation, pre-defining the standard configurations without relying on hints from end users has been raised as a major difficulty within the industry. In this paper, we propose a reconfiguration framework which considers data collected from the use phase in order to generate valid and optimized configurations with regard to stakeholders needs.

Keywords

ReconfigurationConfiguration designSystems Engineering (SE)ComplexityBig data
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 2617 - 2626
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

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