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Integration of data mining and multi-objective optimisation for decision support in production system development
University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)
Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden.
2014 (English)In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 27, no 9, 824-839 p.Article in journal (Refereed) Published
Abstract [en]

Multi-objective optimisation (MOO) is a powerful approach for generating a set of optimal trade-off (Pareto) design alternatives that the decision-maker can evaluate and then choose the most-suitable configuration, based on some high-level strategic information. Nevertheless, in practice, choosing among a large number of solutions on the Pareto front is often a daunting task, if proper analysis and visualisation techniques are not applied. Recent research advancements have shown the advantages of using data mining techniques to automate the post-optimality analysis of Pareto-optimal solutions for engineering design problems. Nonetheless, it is argued that the existing approaches are inadequate for generating high-quality results, when the set of the Pareto solutions is relatively small and the solutions close to the Pareto front have almost the same attributes as the Pareto-optimal solutions, of which both are commonly found in many real-world system problems. The aim of this paper is therefore to propose a distance-based data mining approach for the solution sets generated from simulation-based optimisation, in order to address these issues. Such an integrated data mining and MOO procedure is illustrated with the results of an industrial cost optimisation case study. Particular emphasis is paid to showing how the proposed procedure can be used to assist decision-makers in analysing and visualising the attributes of the design alternatives in different regions of the objective space, so that informed decisions can be made in production systems development.

Place, publisher, year, edition, pages
Taylor & Francis, 2014. Vol. 27, no 9, 824-839 p.
Keyword [en]
Data Mining, Multi-Objective Optimisation, Decision Support, Production Systems
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-10058DOI: 10.1080/0951192X.2013.834481ISI: 000337245200002Scopus ID: 2-s2.0-84901449346OAI: oai:DiVA.org:his-10058DiVA: diva2:752277
Available from: 2014-10-03 Created: 2014-10-03 Last updated: 2016-02-19Bibliographically approved

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Dudas, CatarinaNg, Amos H.C.Pehrsson, Leif
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