The retrieval method of KMS knowledge meshes by complexity analysis

Ren-Zi Yang, Hong-Sen Yan and Li-Li Zhu

International Journal of Production Research, 2016, vol. 54, issue 15, 4599-4616

Abstract: Knowledgeable manufacturing system (KMS) transforms all types of advanced manufacturing modes into corresponding knowledge meshes (KMs) and selects the best combination of KMs to satisfy enterprise requirements. Efficiently retrieving and reconfiguring KMs can reduce the complexity of new KMs gained by self-reconfiguration operations and enhance its practicability. This paper presents the method for measuring the KM complexity based on entropy and that for fuzzily classifying and retrieving for KMs based on granularity. Utilising the intrinsic information of KM, knowledge capacity function based on entropy is introduced to measure the KM complexity, and proved to be a monotone function of the number, measure, coefficient and weight of elements in KM. Properties of the KM operations are kept. Taking quality, quantity and complexity into account, the similarity function is defined. As revealed by our analysis, this function is of similarity both in the sense of matching, and in the mode of gaining KM. Then, the KMs in the KM base are fuzzily clustered. The number of classes is not fixed in advance, but can be dynamically adjusted. Each clustering centre is the best state corresponding to certain demands and has the minimum complexity degree. KM features are quantised in importance using the weight vectors. The search space is determined by centring at the clustering, which converts the problem from fine-grained space to coarse-grained space. Our tests and software package developed have proved the method to be quite effective.

Date: 2016
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2015.1090641 (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:54:y:2016:i:15:p:4599-4616

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2015.1090641

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 ().