Simulering av industriella processer och
logistiksystem
MION40, HT 2012
Simulation Project
Improving Operations at County Hospital
County Hospital wishes to improve the service level of its regular X-ray operation which runs
from 8 am to 8 pm. Patients have identified the total required time as the major service issue in
this process. Management, on the other hand, is concerned with utilization of available resources.
A process design team has been formed to study this problem. The process might be redesigned as
a result of the team’s recommendations.
The team has defined the entry point to the X-ray process to be the instant a patient leaves the
physician’s office en-route to the X-ray lab. The exit point has been defined as the instant at which
the patient and the completed X-ray enter the physician’s office.
There are broadly speaking two types of patients arriving to the X-ray process - emergency and
non-emergency patients (priority level 1 and 2 respectively). The emergency patients arrive
according to a Poisson process with a mean of 4 patients per hour. The non-emergency patients are
registered as they enter and a sample of the arrival time data is provided in the Appendix. So far,
no attempt has been made to further analyze this data, and there is no insight into what the arrival
process looks like.
The team has identified 12 activities in the current X-ray process (see Table 1), which is the same
irrespective of the patient type. The only differences between patient categories are the activity
times and their distributions, specified in Table 3.
It should be noted that: The patient priority levels determine the service order at all the X-ray
activities. Emergency patients (priority 1) come first at the expense of non-emergency patients
(priority 2). However, ongoing service will never be interrupted at the benefit of high priority
patients.
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Table 1. Activities in the X-ray process
Activity
1
2
3
4
5
6
7
8
9
10
11
12
Description
Patient leaves physician’s office with instructions.
Type
Start of the X-ray
process
Patient is taken to the lab by an orderly – on foot, in a Transportation
wheel chair or lying in a bed.
The patient is left in the waiting area outside the XWaiting
ray lab in anticipation of an X-ray technician.
An X-ray technician fills out a standard form based
Business value
on information supplied by the physician and the
added
patient (done outside the X-ray lab). The technician
then leaves the patient, which queues up in front of
the X-ray labs.
The patient enters the X-ray lab, undresses and an X- Value added
ray technician takes the required X-rays (all done in
the X-ray lab).
A dark room technician develops the X-rays.
Value added
(Assume that the patient and the X-ray technician
accompanies the X-rays)
The dark room technician and the X-ray technician
Inspection
check the X-rays for clarity. (Assume that the patient
accompanies his/her X-rays)
If the X-rays are not clear, the patient needs to go
Decision
back to the waiting room in anticipation of repeating
steps 5, 6 and 7. Historically, the probability of
rejecting X-rays has been 25%. If the X-rays are
acceptable the patient proceeds to activity 9 while the
X-rays are put in the outbox, where eventually the
messenger service will pick them up.
Patient waits for an orderly to take him/her back to
Waiting
the physician’s office
Patient is taken back to the physician’s office by an
Transportation
orderly.
A messenger service transfers the X-rays to the
Transportation
physician’s office in batches of five jobs.
Patient and X-rays enter physician’s office together.
End
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The resource data for the X-ray process are specified in Table 2 below.
Table 2. Resource data for X-ray process
Resource
Activities
Orderlies
2 and 10
X-ray technician
4, 5, 6 and 7
X-ray lab
5
Dark room technician
6 and 7
Dark room
6
No. of units available
3
3
2
2
1
The orderlies will always try to take a patient back from the X-ray when they have dropped one
off. Assume that the transportation time back from the X-ray lab is exactly the same as the
transportation time to the X-ray lab. If no patient is ready to go back the orderly will wait for five
minutes, if no patient becomes available during this time the orderly will return to the ward
without a patient. The time for an orderly to walk back without a patient is always 5 minutes. The
orderlies will never go and pick up a patient at the X-ray area without bringing another patient
with them from the ward.
Table 3. Activity times
Activity Patient type
1
2
3
4
5
6
7
8
9
10
11
12
All types
Emergency patients
Non-emergency patients
All types
All types
Emergency patients
Non-emergency patients
All types
All types
All types
All types
Emergency patients
Non-emergency patients
All types
All types
Activity time
distribution
Not Applicable
Uniform
Uniform
Not Applicable
Uniform
Normal
Normal
Normal
Constant
Constant
Not Applicable
Uniform
Uniform
Uniform
Not Applicable
3
Parameter values (minutes)
Not Applicable
Max=9, Min=5
Max=12, Min=5
Not Applicable
Max=6, Min=4
=9, =4
=11, =4
=12, =5
Value=2
Value=0
Not Applicable
Max=9, Min=5
Max=12, Min=5
Max=7, Min=3
Not Applicable
Assignments
Analyzing the current process design
Phase I
a) Draw a flow chart over the X-ray process. The flowchart should describe the process in
detail. A careful documentation and a good understanding of the process will make it
easier to build the simulation model in phase 2. The flowchart should be drawn on the
computer. The most important objective is to convey an unambiguous and detailed
description of the process, using a particular syntax or set of symbols is of less importance.
It is recommended not to proceed with the following questions before you are sure that the
solution to a) describes the problem correctly.
Phase II
b) Develop a simulation model of the process.
 Modeling hint: For batching and un-batching, use the blocks “Batch” and
“Unbatch” from the Discrete Event Library with the option “Preserve uniqueness”
checked.
 Modeling hint: Build the model incrementally based on your flowchart, do not try
to put everything together at once and then test if it works. The keyword is “Baby
steps”
 As a first check that everything works as supposed to it is often useful to run a
shorter simulation with animation. Use different symbols for different types of
items, for example different types of labor and different types of jobs.
c) As a first cut analysis, run a 1 day simulation with Random seed=100, using the correct
activity time distributions. Look at the average cycle time, the throughput, the resource, the
queue and the activity statistics. What seems to be the problems in this process?
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Phase III
d) Analyze the input data regarding non-emergency patients’ inter-arrival times. Use the
statistical tools discussed in class to determine a distribution that adequately describes the
data. The analysis should be done without the help of the “statfit” software available in
Extend.
e) Simulate 30 days of operation and compute the cycle time and daily throughput (average
and standard deviation). Also compute the activity and resource utilization statistics and
queue statistics with 95% confidence intervals (use the statistics blocks in Extend).
Assume that any patients remaining in the system at the end of the day will be taken care
of by the night shift. Every morning the system is considered empty. Are there any
surprises compared to the results in c) above?
f) Assess the performance of the process using the values calculated in e). Where is the
bottleneck? What seems to be the problem in terms of cycle time and throughput?
Suggest and evaluate a new process design
g) Based on your insights about current operations suggest at least three plausible design
changes with the purpose of reducing the average cycle time and increasing the throughput.
h) Investigate the performance of the new process design in terms of the cycle times, and the
daily throughput. Also, look at the resource, activity utilization and queue statistics with
95% confidence intervals as before. What is your conclusion? Is the new design
significantly better than the old process in terms of cycle times and daily throughput (the
conclusion must be statistically supported)? Are there any obvious drawbacks?
Instructions for the final report
Each group should produce a well-written and well-structured typed report (OBS! One report per
group!). The report should explain the problem, your approach to solve it, your findings, results
and conclusions. The questions a)-h) above obviously need to be treated fully and model printout
and data on which you base your conclusions needs to be made available in appendices. The
underlying Extend models should be provided in electronic format via e-mail to Fredrik Olsson.
The report, excluding the electronic Extend models, shall be submitted in paper format to Fredrik
Olsson. Each report must clearly state the name of all the group members.
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Appendix
Arrival data for non-emergency patients
Patient #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Time of arrival
(in minutes from time zero)
1.49
3.01
6.29
7.76
10.87
13.54
45.13
47.03
47.97
52.02
53.81
55.07
57.25
59.56
73.87
92.60
111.60
122.03
128.93
133.67
135.82
138.01
138.53
144.39
149.49
158.34
170.66
173.11
176.59
177.85
Patient #
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
Time of arrival
(in minutes from time zero)
183.77
183.80
185.46
187.54
199.79
213.09
227.22
235.60
259.43
272.97
275.60
286.18
287.90
293.97
297.86
298.56
306.59
321.28
325.51
328.73
329.21
329.50
339.11
343.71
356.54
356.68
366.32
371.05
374.01
374.86
This material is also available in electronic format on the WEB.
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