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Innovative Design and Analysis of Production Systems by Multi-objective Optimization and Data Mining
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. School of Engineering, Jönköping University, Sweden . (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)ORCID iD: 0000-0001-5436-2128
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Sweden . (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-4086-3877
2016 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 50, 665-671 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents an innovative approach for the design and analysis of production systems using multi-objective optimization and data mining. The innovation lies on how these two methods using different computational intelligence algorithms can be synergistically integrated and used interactively by production systems designers to support their design decisions. Unlike ordinary optimization approaches for production systems design which several design objectives are linearly combined into a single mathematical function, multi-objective optimization that can generate multiple design alternatives and sort their performances into an efficient frontier can enable the designer to have a more complete picture about how the design decision variables, like number of machines and buffers, can affect the overall performances of the system. Such kind of knowledge that can be gained by plotting the efficient frontier cannot be sought by single-objective based optimizations. Additionally, because of the multiple optimal design alternatives generated, they constitute a dataset that can be fed into some data mining algorithms for extracting the knowledge about the relationships among the design variables and the objectives. This paper addresses the specific challenges posed by the design of discrete production systems for this integrated optimization and data mining approach and then outline a new interactive data mining algorithm developed to meet these challenges, illustrated with a real-world production line design example.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 50, 665-671 p.
Keyword [en]
Production Systems, Multi-Objective Optimization, Data Mining
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-12815DOI: 10.1016/j.procir.2016.04.159OAI: oai:DiVA.org:his-12815DiVA: diva2:955319
Conference
26th CIRP Design Conference
Available from: 2016-08-25 Created: 2016-08-25 Last updated: 2016-08-25Bibliographically approved

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Ng, Amos H.C.Bandaru, SunithFrantzén, Marcus
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