Graduate Thesis Or Dissertation
 

Effectiveness of using two and three-parameter distributions in place of "best-fit distributions" in discrete event simulation models of production lines

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/0z709005k

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  • This study presents the results of using common two or three-parameter "default" distributions in place of "best fit distributions" in simulations of serial production lines with finite buffers and blocking. The default distributions used instead of the best-fit distribution are chosen such that they are non-negative, unbounded, and can match either the first two moments or the first three moments of the collected data. Furthermore, the selected default distributions must be commonly available (or easily constructed from) distributions in simulation software packages. The lognormal is used as the two-parameter distribution to match the first two moments of the data. The two-level hyper-exponential and three-parameter lognormal are used as three-parameter distributions to match the first three moments of the data. To test the use of these distributions in simulations, production lines have been separated into two major classes: automated and manual. In automated systems the workstations have fixed processing times and random time between failures, and random repair times. In manual systems, the workstations are reliable but have random processing times. Results for both classes of lines show that the differences in throughput from simulations using best-fit distributions and two parameter lognormal is small in some cases and can be reduced in others by matching the first three moments of the data. Also, different scenarios are identified which lead to higher differences in throughput when using a two-parameter default distribution.
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