Title | ||
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Performance evaluation in finite production run-based serial lines with geometric machines |
Abstract | ||
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A production run is usually referred to as a group of identical or similar goods that is produced by a particular manufacturing process. In various manufacturing industries, the manufacturing activity is carried out by deploying a series of production runs of different products according to customer orders. In this paper, we consider finite production run-based manufacturing in serial lines with machines obeying the geometric reliability model and buffers having finite capacity. For one-machine case, exact Markovian analysis approach is used. Closed-form formulae are derived to calculate the transient performance of the production line during the processing of a finite-size production run as well as mean of its completion time. For two- and multi-machine lines, an aggregation-based computationally efficient approach is proposed to approximate the system performance measures. The accuracy of the algorithms developed are justified using numerical simulations. |
Year | DOI | Venue |
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2016 | 10.1109/COASE.2016.7743439 | 2016 IEEE International Conference on Automation Science and Engineering (CASE) |
Keywords | Field | DocType |
finite-size production run-based serial lines,geometric machines,identical goods,similar goods,manufacturing process,manufacturing industries,manufacturing activity,customer orders,finite production run-based manufacturing,geometric reliability model,Markovian analysis,production line transient performance,two-machine lines,multimachine lines,aggregation-based computationally efficient approach,system performance measures,numerical simulations | Mathematical optimization,Manufacturing,Markov process,Production line,Engineering,Transient analysis,Manufacturing process,Reliability model,Reliability engineering | Conference |
ISBN | Citations | PageRank |
978-1-5090-2410-0 | 2 | 0.38 |
References | Authors | |
10 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zhiyang Jia | 1 | 23 | 3.03 |
Liang Zhang | 2 | 121 | 17.53 |
Guorong Chen | 3 | 2 | 0.38 |
Jorge Arinez | 4 | 94 | 18.62 |
Guoxian Xiao | 5 | 104 | 14.79 |