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  4. An aggregate second order continuum model for transient production planning
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An aggregate second order continuum model for transient production planning

Full metadata

Title
An aggregate second order continuum model for transient production planning
Description

Factory production is stochastic in nature with time varying input and output processes that are non-stationary stochastic processes. Hence, the principle quantities of interest are random variables. Typical modeling of such behavior involves numerical simulation and statistical analysis. A deterministic closure model leading to a second order model for the product density and product speed has previously been proposed. The resulting partial differential equations (PDE) are compared to discrete event simulations (DES) that simulate factory production as a time dependent M/M/1 queuing system. Three fundamental scenarios for the time dependent influx are studied: An instant step up/down of the mean arrival rate; an exponential step up/down of the mean arrival rate; and periodic variation of the mean arrival rate. It is shown that the second order model, in general, yields significant improvement over current first order models. Specifically, the agreement between the DES and the PDE for the step up and for periodic forcing that is not too rapid is very good. Adding diffusion to the PDE further improves the agreement. The analysis also points to fundamental open issues regarding the deterministic modeling of low signal-to-noise ratio for some stochastic processes and the possibility of resonance in deterministic models that is not present in the original stochastic process.

Date Created
2015
Contributors
  • Wienke, Matthew (Author)
  • Armbruster, Dieter (Thesis advisor)
  • Jones, Donald (Committee member)
  • Platte, Rodrigo (Committee member)
  • Gardner, Carl (Committee member)
  • Ringhofer, Christian (Committee member)
  • Arizona State University (Publisher)
Topical Subject
  • Applied Mathematics
  • Industrial Engineering
  • Production management--Mathematics.
  • Production Management
  • Industrial engineering--Statistical methods.
Resource Type
Text
Genre
Doctoral Dissertation
Academic theses
Extent
vii, 94 pages : illustrations (some color)
Language
eng
Copyright Statement
In Copyright
Reuse Permissions
All Rights Reserved
Primary Member of
ASU Electronic Theses and Dissertations
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.36022
Statement of Responsibility
by Matthew Wienke
Description Source
Retrieved on Jan. 5, 2016
Level of coding
full
System Created
  • 2015-12-01 07:04:32
System Modified
  • 2021-08-30 01:26:26
  •     
  • 2 years ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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