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discrete (example quantity). In SimioTM software one input model is the time based arrival process, which is called “Time Varying Arrival Rate” and it is presented in the object called source. Another input model is the processing time of each machine presented in the objects server, workstation, combiners, and separators. Randomness is derived from the distribution of the continuous or discrete values that comes from the data gathering; in our case, the processing times of the machines. The steps to find the data distribution for the processing times of each process are the following (Joines and Robets 2012): 1) Collect data 2) Identify candidate distributions 3) For each candidate distribution a) Estimate parameters for the candidate distributions b) Compare candidate distribution to histograms c) Compare the candidate distributions function to the data based function 4) Select the best representation of the input (the input model) Processing times were gathered from the production rate of Plant C and D as mentioned in section 3.3.2 and times collected at the shop floor. 30 times were collected with the use of a stop watch from the shop floor. Then with this 30 values it was calculated the sample size for the by the use of the following formula: [ ]2 Where n= sample size Zα/2= is known as the critical value, the positive value that is at the vertical boundary for the area of in the right tail of the standard normal distribution. 57PDF Image | Impact of Vacuum-Drying on Efficiency of Hardwood Products
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