clinic simulation using ARENA

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Nasser Ameer 11049

Terkee Al-Rodan 10961

Final Project: Case 9

Advanced System Simulation

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1. Introduction

1.1. Description of our project. 1.2. Resources needed in the system. 2. Problem Statement

2.1. Problem Statement, Specifics 2.2. Potential Layout

2.3. Objective of this project 3. ARENA Model

3.1. The Model

3.2. Additions to the system 4. Results

4.1. Results 4.2. Analysis 5. Improvements

5.1. System Layout Redesign. 5.2. Variable Resource allocation. 5.3. New Results

5.4. Comparison 6. Conclusion

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Introduction:

Description of our project:

Our project is based on a case study given to use in class, the first Case 9.

Case 9, required us to simulate a 24/7 Clinic that carried out 5 processes. First,

patients would check in, they are then directed into one of three different departments,

the nurse station, the examination rooms or the lab and X-ray department. When

finished the patients checkout, through the checkout department. Finally, we where

supposed to run the system for thirty 24 hour days.

The following report aims to thoroughly describe a simulation system in terms

of stating the requirements and the creation of that system. After which, it will move

on to stating the results of the system, in terms of time. Finally, this report will then

suggest certain improvements to make the process more efficient and effective.

Resources needed for this system:

Seeing that this is a clinic we will need five basic resources:

 Receptionists: they are responsible for the check in process of the patients.

 Nurse: they are needed to carryout certain task, and help aid the doctors.

 Doctors/Exam Room operators: they are the main resource for this process, in other words they are needed for the treatment of the

patients.

 Lab Technicians: they are the people who operate the machines, such as the X-ray machines

 Check out Administrator: they are the opposite of the receptionist, in that they are responsible for checking out the patients.

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Problem Statement:

Problem Statement, Specifics:

As we described above the problem is to simulate 24/7 clinic for thirty

24-hour days. The main issue with this system is the freedom of the patients to go where

they want after they finish one of the processes, note this my lead to a patient going to

the same station more than once. In other words, once a patient enters the systems

there is no specified path the customer must go on, it is only the probabilities of where

customers can go next that indicates the coming point on the path. The follow points

are the specifics of the system:

 Inter-arrival times are exponentially distributed, with mean 5.95 minutes.

 There are 3 exam rooms

 All Queues follow the FIFO queue discipline.  They require:

o Average total time in system. o Average number of Patients. o The throughput of the system. o Average waiting time of the patients o Resource utilization

o A graph showing the number of patients currently in the clinic. o A counter that counts the throughput of the system.

 The following table contains the processing times for each of the tasks, and the probabilities for each of the possible corresponding

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Potential Layout:

The following image is a potential layout for the clinic:

R= Reception

NS= Nurse Station

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LX= Lab and X-ray

CO= Check Out

Project object:

Our objective for this project is to reduce:

 The total time a customer spends in the system  Reduce the waiting time of the customers  Increase the throughput of the system

 Ensure that all resources are utilized between 40-90%

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ARENA Model:

Using the information above we where able to create the following model,

Please note, that the process times used are all in minutes and have already

been stated above. Furthermore, all decides are two way by chance unless stated

otherwise.

Patients Arrive:

This is done via the create module, with the inter-arrival value of Expo(5.95)

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Sign in and Waiting room:

This is done through the use of a process module, which uses the Seize Delay

Release action and has a one receptionist as a resource. Furthermore, the delay time

was giving in the table above.

Where to Out of Sign in and Waiting Room?

The following block is a decide module, with the true branch entering the

nurse station, with a probability of 98.88 %. The false branch is connected to

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Nurse Station:

This is a process module, with a Seize delay release action and a resource of

one nurse.

Where to out of Nurse Station?

This is another decide, where true leads to examination, with a percentage of

91.67%, and false leads to Lab and X-ray.

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This is a seize delay release process module, with one exam room resource

and a delay as stated above. Furthermore, go to the resource module in the basic

process area, and change the capacity of the exam room to 3.

Where to out of exam room?

This decides is an N-way by chance, where the false lead to check out and the

true leads to the Nurse station, Exam room or Lab and X-ray, respectively.

Lab and X-ray:

This section is a seize delay release process module, with one Lab technician

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Where to out of lab and X-ray?

This is another decide module where true leads to the Nurse station, with an

83.33% chance, and False leads to the checkout counter.

Check Out:

It is a Seize Delay Release process, where there is one Checkout

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Go Home:

This is the dispose module called “Go Home”.

Additions to the system:

As stated above a graph is required to show the current number in the system.

To do so, select the plot icon on the ARENA toolbar, add a new data series with the

name “Patients in Clinic”, and the expression “EntitiesWIP(Patient)” to show us the number of Patient currently in the system. Furthermore, make sure that the x-axis’s

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As for the graphics used, they are added through the use of the resource button

on the toolbar. These were not requested for the simulation but were added for effect.

Finally, the throughput counter is added from the variable button on the tool

bar and counts the number of people who leave the system, i.e. the number out for the

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Results:

Here are the results of the system, see the actual overview report at the back of

the report:

 Average total time in system = 117.88 minutes  Average number of Patients. = 19.5 patients  The throughput of the system. = 7,151 Patients  Average waiting time of the patients = 82.17 minutes  Resource utilization: Resource Utilization Nurse 93.350% Receptionist 71.810% Lab Technicians 62.250% Check out Administrator 88.570% Exam Room 91.770% Analysis:

From the data above we can see that:

0.000% 10.000% 20.000% 30.000% 40.000% 50.000% 60.000% 70.000% 80.000% 90.000% 100.000%

Utilization

Utilization

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 Around 70% of the time customers are waiting in the system.

Non-waiting time 35.710

Wait time 82.17

Total time 117.88 Percentage wait time 69.706%

 The Nurses and the exam rooms are over Utilized

Non-waiting time Wait time

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Improvement:

System Layout Redesign:

To improve the system we decided to structure it more, in other words,

recreate the system by making it more linear, through the reduction in the number of

paths that can be taken by the customer. We did this by shifting the position of the

first nurse station, so that it is after the reception, making it mandatory for the

customer to go to a nurse station straight after check in. By doing so, the patient will

then go in to a decide, where 91.7% of the patients go to the examination station after

he/she is finished from the Nurse station, and the rest of the patients can go to the lab

and X-ray department.

Furthermore, once the customer finishes examination then a new decide is

added, where 47.06% of customers are re-examined. The rest of the customers will

then move on to a new nurse station, again making it mandatory for the patient to

enter this process after examination.

After which the customer go through another decide, where 95.6% of patients

checkout, the other customers go to the lab and X-ray departments. Where, after they

finish, they are presented with another decide, through which 83.33% of the

customers go to the first Nurse station, and the rest go to the checkout.

Please note that due to the new nurse station, Nurse Station 2, the capacity of

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Variable resource Allocation:

After we ran the new system we saw that the queue length for the checkout

station can become very crowded. Therefore, we added an administrator that will

dispatch up to 2 on-call checkout administrators, should the number of people in the

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Results:

 Average total time in system = 68.03 minutes  Average number of Patients. = 11.49 patients  The throughput of the system. = 7,295 Patients  Average waiting time of the patients = 32.32 minutes

Comparison: Utilization: Resource Utilization Nurse 75.780% Receptionist 73.180% Lab Technicians 52.920%

Check out Administrator 88.920%

Exam Room 78.460%

As you can see all values are within the optimal range of 45-90% in terms of

utilization. Which is better than the original.

0.000% 10.000% 20.000% 30.000% 40.000% 50.000% 60.000% 70.000% 80.000% 90.000%

Utilization

Utilization

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Time:

Reduction

Non-waiting time 35.710 Same Wait time 32.32 39.333% Total time 68.03 57.711% Percentage wait time 47.508%

As you can see in the table, the total and waiting times have decreased

drastically, making this a more efficient system.

93.350% 71.810% 62.250% 88.570% 91.770% 75.780% 73.180% 52.920% 88.920% 78.460% 0.000% 10.000% 20.000% 30.000% 40.000% 50.000% 60.000% 70.000% 80.000% 90.000% 100.000%

Nurse Receptionist Lab Technitians Check out

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Non-waiting time Wait time

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Conclusion:

In conclusion, this report simulated a 24 hour 7 days a week clinic for 30 days.

It then analyzed the results obtained from that simulation and tried to improve it.

Furthermore, the improvement included a redesign of the system, through the addition

of an extra nurse station, which would make it more linear, and the addition of a

administrative official that would bring in up to two more checkout administrators to

help with spikes in demand. Finally, by carrying out these improvements we were

able to reduce the total time and waiting time of the customer, and ensure optimal

Figure

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References

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