Development of operational complexity measure for selection of optimal layout design alternative
Vladimir Modrak and
Zuzana Soltysova
International Journal of Production Research, 2018, vol. 56, issue 24, 7280-7295
Abstract:
Characterising existing approaches to operational complexity, it can be stated that for those metrics are characterful different factors used as variables such as product structure, machine composition, number of technological functions performed by machine, and others. Moreover, the complexity metrics using the information content as a basis can be divided into two groups: those which define complexity as an absolute entropy quantity, and metrics defining complexity as the relative entropy magnitude. Our view on the operational complexity is based on an assumption that process complexity value obtained as a sum of the partial complexities is not so important than a balanced operational complexity value expressing relative quantity to equilibrium levels. Accordingly, this paper introduces the novel operational complexity measure that initially identifies operational complexities of individual machines based on the number of parts, machines and operations. In the subsequent steps, these sub-measures are used to define summary complexity measure involving two balanced operational complexity characteristics. The novel measure can be effectively used to find the most suitable layout design alternative. For the purpose to prove its effectiveness, on two practical cases where tested its practicability by comparing it to the complexity indicator expressing the sum of the partial complexities.
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2018.1456696 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:56:y:2018:i:24:p:7280-7295
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20
DOI: 10.1080/00207543.2018.1456696
Access Statistics for this article
International Journal of Production Research is currently edited by Professor A. Dolgui
More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().