Queueing up in R, continued

October 20, 2011
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(This article was first published on Probability and statistics blog » r, and kindly contributed to R-bloggers)

Shown above is a queueing simulation. Each diamond represents a person. The vertical line up is the queue; at the bottom are 5 slots where the people are attended. The size of each diamond is proportional to the log of the time it will take them to be attended. Color is used to tell one person from another and doesn’t have any other meaning. Code for this simulation, written in R, is here. This is my second post about queueing simulation, you can find the first one, including an earlier version of the code, here. Thanks as always to commenters for making suggestions on how to improve the code.

A few notes about the simulation:

  • Creating an animation to go along with your simulation can take a while to program (unless, perhaps, you are coding in Flash), and it may seem like an extra, unnecessary step. But you can often learn a lot just by “watching”, and animations can help you spot bugs in the code. I noticed sometimes smaller diamonds hung around for much longer then I expected, which led me to track down a tricky little error in the code.
  • As usual, I’ve put all of the configuration options at the beginning of the code. Try experimenting with different numbers of intervals and tellers/slots, or change the mean service time.
  • If you want to run the code, you’ll need to have ImageMagick installed. If you are on a PC, make sure to include the full path to “convert”, since Windows has a built-in convert tool might take precedence. Also, note how the files that represent the individual animation cells are named. That’s so that they are added in the animation in the right order, naming them sequentially without zero at the beginning failed.
  • The code is still a work in progress, it needs cleanup and I still have some questions I want to “ask” of the simulation.

To leave a comment for the author, please follow the link and comment on his blog: Probability and statistics blog » r.

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