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A comparative study of production control mechanisms using simulation-based multi-objective optimisation
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.ORCID iD: 0000-0003-0111-1776
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
2012 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 50, no 2, 359-377 p.Article in journal (Refereed) Published
Abstract [en]

There exist many studies conducted to compare the performance of different production control mechanisms (PCMs) in order to determine which one performs the best under different conditions. Nonetheless, most of these studies suffer from the problems that the PCMs are not compared with their optimal parameter settings in a truly multi-objective context. This paper describes how different PCMs can be compared under their optimal settings through generating the Pareto-optimal frontiers, in the form of optimal trade-off curves in the performance space, by applying evolutionary multi-objective optimisation to simulation models. This concept is illustrated with a bi-objective comparative study of the four most popular PCMs in the literature, namely Push, Kanban, CONWIP and DBR, on an unbalanced serial flow line in which both control parameters and buffer capacities are to be optimised. Additionally, it introduces the use of normalised hyper-volume as the quantitative metric and confidence-based significant dominance as the statistical analysis method to verify the differences of the PCMs in the performance space. While the results from this unbalanced flow line cannot be generalised, it indicates clearly that a PCM may be preferable in certain regions of the performance space, but not others, which supports the argument that PCM comparative studies have to be performed within a Pareto-based multi-objective context.

Place, publisher, year, edition, pages
Taylor & Francis, 2012. Vol. 50, no 2, 359-377 p.
Keyword [en]
production control mechanisms, stochastic simulation, multi-objective optimisation, optimal buffer allocation
National Category
Engineering and Technology
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-6219DOI: 10.1080/00207543.2010.538741ISI: 000303582300005Scopus ID: 2-s2.0-84860008051OAI: oai:DiVA.org:his-6219DiVA: diva2:543453
Available from: 2012-08-08 Created: 2012-08-08 Last updated: 2015-12-18Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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More styles
Language
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