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MITIGATING UNCERTAINTY IN CONCEPTUAL DESIGN USING OPERATIONAL SCENARIO SIMULATIONS: A DATA-DRIVEN EXTENSION OF THE EVOKE APPROACH

Published online by Cambridge University Press:  19 June 2023

Alessandro Bertoni
Alessandro Bertoni*
Affiliation:
Blekinge Institute of Technology
*
Bertoni, Alessandro, Blekinge Institute of Technology, Sweden, alessandro.bertoni@bth.se

Abstract

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The paper presents an approach where the iterative replication of Discrete Event Simulations on future operational scenarios is used to derive data-driven design merit functions. The presented contribution proposes an extension of the EVOKE (Early Value Oriented Design Exploration with Knowledge Maturity) approach determining when and how the experience-based judgment about maximization, minimization, optimization, and avoidance functions, correlating value drivers and quantified objectives, can be substituted by data-driven mathematical functions obtained by scenarios simulations. The approach is described through a simplified case concerning the development of autonomous electric vehicles to complement the public transport system in the city of Karlskrona in Sweden. The consideration of value drivers and quantified objectives presented is meant to support a preliminary screening of potential design configurations to support the definition of high-level product and system-related functional requirements, to be run before a more detailed conceptual design analysis.

Keywords

Conceptual designEVOKEDecision makingDiscrete Event SimulationSystems Engineering (SE)
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2665 - 2674
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), 2023. Published by Cambridge University Press

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