While donating blood on campus, a management science graduate student noticed that the mobile blood donor clinic set up at his university’s community centre was a congested tandem queuing system. Finding one-and-one-half hours too long for donors to wait, the student considered how the process could be reduced by at least half an hour. He needed to devise a reasonably precise model to represent the donor flow in the clinic. Using either the mode or the average service times supplied by the nurses, the student could build a relatively straightforward discrete-event simulation model to identify bottlenecks and improve the donor flow.
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