Week 12 Homework
Due Nov 17 at 11:59pm Points 17 Questions 17
Available after Nov 8 at 8am Time Limit None
Attempt History
Attempt
Time
Score
LATEST Attempt 1
1,965 minutes 13 out of 17
Score for this quiz: 13 out of 17 Submitted Nov 17 at 8:18pm This attempt ...
Week 12 Homework
Due Nov 17 at 11:59pm Points 17 Questions 17
Available after Nov 8 at 8am Time Limit None
Attempt History
Attempt Time Score
LATEST Attempt 1 1,965 minutes 13 out of 17
Score for this quiz: 13 out of 17
Submitted Nov 17 at 8:18pm
This attempt took 1,965 minutes.
Question 1 pts
(Lesson 8.1: Introduction to Input Modeling.) It's GIGO time! Let's consider
an queueing system with Exp( ) interarrivals and Exp( ) FIFO
services at a single server. You may recall from some class (either this one
or stochastic processes) that the steady-state expected cycle time (i.e., the
time that the customer is in the system, including wait + service) is
.
If you were to try this out in Arena, let's say with
interarrivals and services (note the notation change
between my usual "Exp" and Arena's ), then we'd get
. Go ahead, see for yourself in Arena, but make
sure that you run the system for 100,000 or so customers so that you can be
sure that you're in steady-state!
Finally, here's the GIGO question, which will show what can happen when
you mis-model a component of your process: What is the (approximate)
steady-state expected cycle time if you have i.i.d. interarrivals
instead of interarrivals? Note that both interarrival
distributions have the same mean (10), but that doesn't necessarily imply
that they'll have the same expected cycle times. Hint: You may want to use
Arena as described above.
(c). The case has waaaay smaller tails than the , so it's
reasonable to assume that the cycle times will tend to be lower for the case. In fact, after 10
average time of 23.5. Thus, (c) is the right answer.
Question 2 pts
(Lesson 8.2: Identifying Distributions.) Let's play Name That Distribution!
The number of times a "3" comes up in 10 dice tosses.
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