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Integration of data...
Integration of data mining and multi-objective optimisation for decision support in production system development
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- Dudas, Catarina (author)
- Högskolan i Skövde,Forskningscentrum för Virtuella system,Produktion och Automatiseringsteknik, Production and Automation Engineering,Högskolan i Skövde, Forskningscentrum för Virtuella system
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- Ng, Amos H.C. 1970- (author)
- Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Produktion och Automatiseringsteknik, Production and Automation Engineering,Högskolan i Skövde, Institutionen för ingenjörsvetenskap
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- Pehrsson, Leif (author)
- Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Produktion och Automatiseringsteknik, Production and Automation Engineering,Högskolan i Skövde, Institutionen för ingenjörsvetenskap
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- Boström, Henrik (author)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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(creator_code:org_t)
- Taylor & Francis, 2014
- 2014
- English.
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In: International journal of computer integrated manufacturing (Print). - : Taylor & Francis. - 0951-192X .- 1362-3052. ; 27:9, s. 824-839
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Produktionsteknik, arbetsvetenskap och ergonomi (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Production Engineering, Human Work Science and Ergonomics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
Keyword
- Data Mining
- Multi-Objective Optimisation
- Decision Support
- Production Systems
- Technology
- Teknik
- Production and Automation Engineering
- Produktion och automatiseringsteknik
- Computer and Systems Sciences
Publication and Content Type
- ref (subject category)
- art (subject category)
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