Solution Manual for Simulation with Arena 6th Edition by Kelton-08

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Exercise 2-8 Solution file from Kelton/Sadowski/Zupick, Simulation With Arena, 6th edition, McGraw-Hill, 2015
Introduce a new event type (Down) and schedule it on initialization to happen at time 4; the extra event record is shaded in the table below. This does nothing to the state
variables or statistical accumulators until time 4 rolls around and the Down event is executed. At that time, the time of departure of the part in service (entity no. 2) is changed
from its prior value (4.66) to that plus the 4-minute downtime, or 4.66 + 4 = 8.66 (new event time shaded in the event calendar at that time). We also schedule an event for the
drill press to come back up (Up) at time 4 + 4 = 8 (event record shaded). In this particular realization, having such an “Up” event might not be deemed necessary, but in general it
could be in the case that the Down event happened when the machine happened to be idle, in which case we’d need to define it as busy at that time (blocking arrivals during the
downtime from entering service), and when it comes back up execute logic to release the first part in queue (if any) to begin service. The calculations in the table below are
similar to what’s in Section 2.4.3 so we leave it to you to recreate this table and check your work. Here’s a crude plot of the number-in-queue curve:
The final output performance measures are:
Total production = 4
Average waiting time in queue = 27.17/5 = 5.43 minutes per part (5 parts)
Maximum waiting time in queue = 12.16 minutes
Average total time in system = 31.58/4 = 7.90 minutes per part (4 parts)
Maximum total time in system = 12.78 minutes
Time-average number of parts in queue = 29.09/20 = 1.45 parts
Maximum number of parts in queue = 3 parts
Drill-press utilization = 20.00/20 = 1.00
Comparing these results to those in Table 2-3, we see that the downtime had the effect of reducing production and increasing congestion ... OK, maybe not surprising, but it would
have been hard to quantify this without the simulation. A legitimate question (that we hope you’re asking yourself) is whether the observed differences are statistically significant
... stay tuned (Exercise 6-18).
Just-Finished Event Variables Attributes Statistical Accumulators Event Calendar
Entity Time Event Arrival Times:
No. t Type Q(t) B(t) (In Queue) In Service P N WQ WQ* TS TS* Q Q* B [Entity No., Time, Type]
  [1, 0.00, Arr]
– 0.00 Init 0 0 ( ) – 0 0 0.00 0.00 0.00 0.00 0.00 0 0.00 [–, 4.00, Down]
[–, 20.00, End]
[2, 1.73, Arr]
1 0.00 Arr 0 1 ( ) 0.00 0 1 0.00 0.00 0.00 0.00 0.00 0 0.00 [1, 2.90, Dep]
[–, 4.00, Down]
[–, 20.00, End]
[1, 2.90, Dep]
2 1.73 Arr 1 1 (1.73) 0.00 0 1 0.00 0.00 0.00 0.00 0.00 1 1.73 [3, 3.08, Arr]
[–, 4.00, Down]
[–, 20.00, End]
This file was downloaded
from the Solutions area of
the website for the 6th ed.
of "Simulation With Arena"
by Kelton, Sadowski, and
Zupick, McGraw-Hill, 2015.