Characterization and Monitoring of Nonlinear Dynamics and Chaos in Manufacturing Enterprise Systems

S. R. T. Kumara and S. T. S. Bukkapatnam
Additional contact information
S. R. T. Kumara: Pennsylvania State University
S. T. S. Bukkapatnam: Oklahoma State University

Chapter Chapter 5 in Network Science, Nonlinear Science and Infrastructure Systems, 2007, pp 99-122 from Springer

Abstract: Abstract Much of the complexity in modern enterprises emerges from the nonlinear and likely chaotic dynamics of the underlying processes. These processes are defined over multiple scales of system granularity, for e.g., supply chain-level, through shop floors, down to a machine or a core physical operation level. Characterization of this complexity is imperative for improving predictability of quality and performance in modern physical and engineered systems. In this paper we present some theoretical developments and tools aimed at advancing the applications of nonlinear dynamic systems principles in manufacturing processes and systems, with specific emphasis on characterizing and harnessing chaos in these complex systems. We examine the current developments in addressing predictability in two important facets of a manufacturing enterprise, namely, process level characterization and monitoring, and systems level characterization. For each case, we concisely evaluate the relevant alternative approaches and layout certain open issues. We hope that this paper will spur further development of methodologies adapting nonlinear dynamics and chaos principles for advancing various aspects of manufacturing enterprises.

Keywords: intelligent agents; manufacturing; fractals (search for similar items in EconPapers)
Date: 2007
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:isochp:978-0-387-71134-8_5

Ordering information: This item can be ordered from
http://www.springer.com/9780387711348

DOI: 10.1007/0-387-71134-1_5

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().