Possibility and evidence theory‐based design optimization: an overview

Abstract

Purpose

To survey the approaches to design optimization based on possibility theory and evidence theory comparatively, as well as their prominent characteristics mainly for epistemic uncertainty.

Design/methodology/approach

Owing to uncertainties encountered in engineering design problems and limitations of the conventional probabilistic approach in handling the impreciseness of data or knowledge, the possibility‐based design optimization (PBDO), evidence‐based design optimization (EBDO) and their integrated approaches are investigated from the viewpoints of computational development and performance improvement. After that, this paper discusses the fusion technologies and an example of integrated approach in reliability to reveal the qualitative value and efficiency.

Findings

It is recognized that more conservative results are obtained with both PBDO and EBDO, which may be appropriate for design against catastrophic failure compared with the probability‐based design. Furthermore, the EBDO design may be less conservative compared with the PBDO design.

Research limitations/implications

How to perfect already‐existing integration approaches in a more generally analytical framework is still an active domain of research.

Practical implications

The paper is a holistic reference for design engineers and industry managers.

Originality/value

The paper is focused on decomposition strategies and fusion technologies, especially addressing epistemic uncertainty for large‐scale and complex systems when statistical data are scarce or incomplete.

Citation

He, L. and Qu, F. (2008), "Possibility and evidence theory‐based design optimization: an overview", Kybernetes, Vol. 37 No. 9/10, pp. 1322-1330. https://doi.org/10.1108/03684920810907616