his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Interleaving Innovization with Evolutionary Multi-Objective Optimization in Production System Simulation for Faster ConvergenceOptimization
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (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. (Production and Automation Engineering)
Stockholm University, Sweden.
Michigan State University, USA.
2013 (English)In: Learning and Intelligent Optimization: 7th International Conference, LION 7, Catania, Italy, January 7-11, 2013, Revised Selected Papers / [ed] Giuseppe Nicosia, Panos Pardalos, Berlin, Heidelberg: Springer Berlin/Heidelberg, 2013, 1-18 p.Chapter in book (Refereed)
Abstract [en]

This paper introduces a novel methodology for the optimization, analysis and decision support in production systems engineering. The methodology is based on the innovization procedure, originally introduced to unveil new and innovative design principles in engineering design problems. The innovization procedure stretches beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. By integrating the concept of innovization with simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis. The uniqueness of the approach introduced in this paper lies in that decision rules extracted from the multi-objective optimization using data mining are used to modify the original optimization. Hence, faster convergence to the desired solution of the decision-maker can be achieved. In other words, faster convergence and deeper knowledge of the relationships between the key decision variables and objectives can be obtained by interleaving the multi-objective optimization and data mining process. In this paper, such an interleaved approach is illustrated through a set of experiments carried out on a simulation model developed for a real-world production system analysis problem.

Place, publisher, year, edition, pages
Berlin, Heidelberg: Springer Berlin/Heidelberg, 2013. 1-18 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 7997
Keyword [en]
Innovization, Multi-Objective Optimization, Data Mining, Production Systems
National Category
Computer Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-10059DOI: 10.1007/978-3-642-44973-4_1Scopus ID: 2-s2.0-84890892997ISBN: 978-3-642-44972-7 ISBN: 978-3-642-44973-4 OAI: oai:DiVA.org:his-10059DiVA: diva2:752297
Available from: 2014-10-03 Created: 2014-10-03 Last updated: 2015-12-18Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Ng, Amos H. C.Dudas, CatarinaKalyanmoy, Deb
By organisation
School of Engineering ScienceThe Virtual Systems Research Centre
Computer Science

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 634 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf