Abstract
Complexity arises in different fields of application. The increasing number of variables and system responses used to describe an experimental problem limits the applicability of classical approaches from Design of Experiments (DOE) and Sequential Experimental Design (SED). In this situation, more effort should be put into developing methodological approaches for complex multi-response experimental problems. In this work, we develop a novel design technique based on the incorporation of the Pareto optimality concept into the Bayesian sequential design framework. One of the crucial aspects of the approach involves the selection method of the next design points based on current information and the chosen system responses. The novel sequential approach has been tested on a simulated case study.
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Acknowledgements
This work has been carried out as a part of the FIDEAS project (Fabbrica Intelligente per la Deproduzione Avanzata e Sostenibile) co-funded within the Framework Agreement between Regione Lombardia and CNR.
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Borrotti, M., Pievatolo, A. (2016). A Multi-objective Bayesian Sequential Design Based on Pareto Optimality. In: Kunert, J., Müller, C., Atkinson, A. (eds) mODa 11 - Advances in Model-Oriented Design and Analysis. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-31266-8_6
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DOI: https://doi.org/10.1007/978-3-319-31266-8_6
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