Using univariate Be´zier distributions to model simulation input processes
WSC '93 Proceedings of the 25th conference on Winter simulation
Recent developments in input modeling with Bézier distributions
WSC '96 Proceedings of the 28th conference on Winter simulation
Bayesian model selection when the number of components is unknown
Proceedings of the 30th conference on Winter simulation
Simulation: The Practice of Model Development and Use
Simulation: The Practice of Model Development and Use
Simulation input modeling: prior and candidate models in the Bayesian analysis of finite mixtures
Proceedings of the 35th conference on Winter simulation: driving innovation
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
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Machine failure is often an important factor in throughput of manufacturing systems. To simplify the inputs to the simulation model for complex machining and assembly lines, we have derived the Arrows classification method to group similar machines, where one model can be used to describe the breakdown times for all of the machines in the group and breakdown times of machines can be represented by finite mixture model distributions. The Two-Sample Cramér-von Mises statistic is used to measure the similarity of two sets of data. We evaluate the classification procedure by comparing the throughput of a simulation model when run with mixture models fitted to individual machine breakdown times; mixture models fitted to group breakdown times; and raw data. Details of the methods and results of the grouping processes will be presented, and will be demonstrated using an example.