Complexity metrics for mixed model manufacturing systems based on information entropy

Andres G. Abad and Jionghua Jin

International Journal of Information and Decision Sciences, 2011, vol. 3, issue 4, 313-334

Abstract: Mixed model manufacturing systems are increasingly used to meet global competition by providing a broad variety of products to customers. The increase of product variety adds more complexity to production processes, thus, leading to a negative effect on the performance of production processes. Therefore, it is of great interest to effectively measure such complexity and to quantify its effect on manufacturing system performance. In this paper, a set of complexity metrics are proposed for measuring the complexity of different elements in a manufacturing system. These metrics were defined by constructing a linkage with the communication system's framework. Different from those existing complexity measures defined in the literature, this paper considers production quality into the measure of the process capability on handling the complexity induced by the input demand variety. Examples are given in the paper to discuss different properties of the defined metrics and their potential applications.

Keywords: complexity metrics; manufacturing systems; information theory; information entropy; mutual information; product variety; communication systems; quality; coefficient constraint; mixed model assembly lines; MMAL; production quality; process capability. (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=43025 (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:ids:ijidsc:v:3:y:2011:i:4:p:313-334

Access Statistics for this article

More articles in International Journal of Information and Decision Sciences from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().