SMA lab Manual JAN 2021.pdf

Department of Industrial Engineering & Management
BMS College of Engineering Bangalore-560 019
LABORATORY INFORMATION BOOK
(Autonomous Stream)
VI SEMESTER I.E.M.
SIMULATION MODELLING & ANALYSIS LAB
(20IM6DLSMA)
Prepared by:
Dr. Ramesh K T
Prathap N
2021

20IM6DLSMA SIMULATION MODELLING & ANALYSIS LAB
Dept. of Indl. Engg. & Mgmt., B.M.S. College of Engineering, Bengaluru -19 - 2 -
SIMULATION MODELLING & ANALYSIS LAB
Sub Code : 20IM6DLSMA CIE Marks : 50
Hours/week : 02 Exam Hours : 03
SEE Marks : 50
Development of simulation models using the simulation language / packages studied for
systems Such as, Queuing systems, Production systems, Inventory systems, Maintenance and
Replacement systems, Investment analysis and Networks.
1. Solving Monte Carlo simulation problems using MS Excel (minimum 4 exercise)
2. Input analysis of Simulation data by statistical tools
3. Simulation of Service system considering Breaks, Schedules and resource failure etc
4. Activity based costing in Simulation
5. Simulation of manufacturing system with multiple servers and failures
6. Simulation of Transport system considering Forklift and conveyor
7. Supply chain simulation
8. Network simulation.
9. Statistical Analysis of Simulation models ( output analysis)
10. Simulation of Bank Queue model
11. Simulation of Project Execution
12. Simulation of Inventory Management

Exercise - 1
(a) Generate a data file containing 50 points for an Normal distribution with parameters:
mean=12, standard deviation = 2. Once you have the data file, perform a Fit all to find the
“best” fit from among the available distributions. Repeat this process for 500, 5000 and
25,000 data points, using the same Normal parameters. Compare the results of the fit all for
the different sample sizes.
(b) Generate 100 random numbers using RAND() in MS Excel and identify the best fit for the
generated data using input analyzer software.
(c) Generate 100 random numbers having a range of 2 to 99 using RAND() in MS Excel and
identify the best fit for the generated data using input analyzer software.
Exercise – 2
(a) Philips India is engaged in manufacturing different types of equipment’s by various
customers. The company has two assembly lines to produce its product. The processing time
for each line is regarded as a random variable and is described by the following distribution.
Processing Time
(Minutes)
Assembly X Assembly Y
40
42
44
46
48
0.10
0.15
0.40
0.10
0.25
0.20
0.40
0.20
0.15
0.05
Using random numbers generate data on the processing times for 30 units of the items and
also compute the expected processing times for the product.
(b) Use Monte Carlo approach to determine the value of Pi considering 95% confidence
Interval
D

Exercise – 3
(a) A classical inventory problem concerns the purchase and sale of newspapers. The paper
seller buys the papers for 33 paise each and sells them for 50 paise each. Newspapers not
sold at the end of the day are sold as scrap for 5 paise each. Newspapers can be purchased in
bundles of 10. Thus, the paper seller can buy 50, 60, and so on. There are three types of news
days, (i) good, (ii) fair, and (iii) poor with probabilities of 0.35, 0.45, and 0.20, respectively.
The distribution of papers demanded on each of these days is given in Table below:
Newspaper seller has the policy to purchase 70 newspapers daily, determine the demand for
the next 20 days and also determine the profits obtained from sales each day.
(b) Production line turns out about 50 truck per day, fluctuations occur for many reasons. The
production can be described by a probability distribution function as follows:
Finished trucks are transported by train at the end of the
day. If the train capacity is only 51. What will be the
average number of trucks waiting to be shipped and what
will be the average number of empty spaces on the train?
Run the simulation for the next 20 days and also calculate
the expected Production / day.
Production / day Probability
45 0.03
46 0.05
47 0.07
48 0.10
49 0.15
50 0.20
51 0.15
52 0.10
53 0.07
54 0.05
55 0.03

Exercise - 4
(a) Simulate a simple processing system which consists of a drilling machine and the
processing time varies according to Triangular distribution of 1 ± 4 minutes. The part enters
the system with a random exponential value of 3 minutes. And then leaves the system. All
time units are in minutes. Animate the resource and queue. Simulate the process for 6
hours. Plot number waiting at drilling center queue and number busy at drill press.
(i) Modify the above model with all of the following changes:
 Add a second machine to which all parts go immediately after exiting the first
machine for a separate kind of processing (rewash). Processing times at the
second machine are the same as for the first machine. Gather all statistics as
before, plus the time in queue, queue length and utilization at the second machine.
 Immediately after the second machine, there’s a pass fail inspection that takes a
constant 5 minutes to carry out and has an 70% chance of passing result; queuing
is possible at inspection, and the queue is first in and first out. All parts exit the
system regardless of whether they pass the test. Count number that fail and
number that pass, and gather statistics on the time in queue, queue length and
utilization at inspection center.
 Add plots to track the queue length and number busy at all three stations.
 Run the simulation for 480 minutes.
(b) In the above exercise suppose that parts that fail inspection after being washed are sent
back and rewashed, instead of leaving; such re-washed parts must then undergo the same
inspection, and have the same probability of failing. There’s no limit on how many times a
given part might have to loop back through the washer. Run this model under the same
conditions and compare the results for the time in queue, queue length and utilization at the
inspection center.
(i) In the above problem suppose the inspection can result in one of the three outcomes; pass
(probability 0.7) fail (probability 0.1) and rewash (probability 0.2). Failures leave
immediately, and rewashes loop back to the washer. The above probabilities hold for
each part undergoing inspection, regardless of its past history. Count the number that fail
and number that pass and gather statistics.
(ii) Suppose that instead of having a single source of parts, there are three sources of arrival,
one for each of the three different kinds of parts that arrive: blue, green and red. For each
color arriving part, inter-arrival times for blue parts follows poison distribution with a
mean of 15 minutes. Inter-arrival times for green parts follows normal with mean of 4

mins. & std. deviation of 0.5 mins and the batch size is of 10, The inter-arrival times for
red parts follows discrete distribution of 0,1,2,3 with equal probabilities.
Run the simulation for 480 minutes. Gather all the statistics as before. (Note: processing
times for all of the three kinds of parts remain the same).
Report the following (for above models)
 The average total time in the system (part) and
 Utilization of drill press
 The last part number which entered the system
 Number of parts which leaves the system
 Average and maximum number of parts in process (wip)
 Make 5 replications of the above simulation. And observe the changes in output.
Tabulate the readings.

Exercise – 5
The system represents the final operations of the production of two different sealed electronic
units, shown in fig. The arriving parts are cast metal cases that have already been machined
to accept the electronic parts.
The first units, called Part A, are produced in an adjacent department, outside the bounds of
this model, with Inter-arrival times to our model being exponentially distributed with a mean
of 5 (all times are in minutes). Upon arrival, they’re transferred (instantly) to the Part A Prep
area, where the mating faces of the cases are machined to assure a good seal, and the part is
then deburred and cleaned; the process time for the combined operation at the Part A Prep
area follows a Tria (1,4,8) distribution. The part is then transferred (instantly, again) to the
sealer.
The second units, called Part B, are produced in a different building, also outside this model’s
bounds, where they are held until a batch of four units is available; the batch is then sent to
the final production area we are modeling. The time between the arrivals of successive
batches of Part B to our model is exponential with a mean of 30 minutes. Upon arrival at the
Part B Prep area, the batch is separated into the four individual units, which are processed
individually from here on, and the individual parts proceed (instantly) to the Part B Prep area.
The processing at the Part B Prep area has the same three steps as at the Part A Prep area,
except that the process time for the combined operations follows a Tria (3,5,10) distribution.
The part is then sent (instantly) to the sealer.
At the sealer operation, the electronic components are inserted, the case is assembled and
sealed, and the sealed unit is tested. The total process time for these operations depends on
the part type: TRIA (1,3,4) for Part A and WEIB (2.5,5.3) for Part B (2.5 is the scale
parameter  and 5.3 is the shape parameter ) Ninety-one percent of the parts pass the
inspection and are transferred immediately to the shipping department; whether a part passes
s independent of whether any other parts pass. The remaining parts are transferred instantly
20%
80%
9%
Part A
EXPO(5)
Part B
Batches of 4
EXPO(30)
Part A Prep
Part B Prep
Sealer
Part A TRIA (1,3,4)
Part B WEIB (2.5,5.3)
Rework
91%
Scrapped
Salvaged
and
Shipped
Shipped

to the rework area where they are disassembled, repaired, cleaned, assembled, are re-tested.
Eighty percent of the parts processed at the reworked parts, and the rest are transferred
instantly to the scrap area. The time to rework a part follows an exponential distribution with
mean of 45 minutes and is independent of part type and the ultimate disposition (salvaged
and scrapped).
of the parts processed at the reworked parts, and the rest are transferred instantly to the scrap
area. The time to rework a part follows an exponential distribution with mean of 45 minutes
and is independent of part type and the ultimate disposition (salvaged and scrapped).
Collect statistics in each area on resource utilization; number in queue, time in queue, and the
cycle time (or total time in system) separated out by shipped parts, salvaged parts, or
scrapped parts. Run the simulation for four consecutive 8-hour shifts for 5 replications.
Exercise - 6
A production system consists of four serial automatic workstations. Parts arrive at
exponential passion with the mean of 3 mins. All transfer times are assumed to be zero and
all processing times are constant. There are two types of failures: major and jams. The data
for this system are given in the table below (all times are in minutes). Use exponential
distributions for the uptimes and uniform distributions for repair times (for instance-repairing
jams at workstation 3 is UNIF (2.8,4.2). Run your simulation for 10,000 minutes to
determine the percent of time each resource spends in the failure state and the ending status
of each workstation queue.
Workstation
Number
Process Time
Major Failure Means Jams Means
Uptimes Repair Uptimes Repair
1 8.5 475 20,30 47.5 2.0,3.0
2 8.3 570 24,36 57 2.4,3.6
3 8.6 665 28,42 66.5 2.8,4.2
4 8.6 475 20,30 47.5 2.0,3.0
Exercise - 7
A firm that sells product Z under a competition market which does not influence price and
the company wants to study the probability distribution for the profit of this product and the
probability that the firm will lose money when marketing it. The profit, Income and Expenses
are as given below :

Total Profit (TP) = Total Income – Total Expense
Total Income = Quantity Demanded (Q) * Price (P)
Total Expense = [Quantity (Q) * Variable Cost (VC)] + Fixed Cost (FC)
The quantity demanded (Q) follows uniform distribution of 7000, 10000 units. The variable
cost (VC) follows normal distribution (8,4) truncated on both the sides with a minimum of 2
and maximum of 9. The Price (P) follows a normal distribution (12,2) truncated on the lest
side with minimum of 2. the fixed cost is Rs. 10000.
Run the simulation and determine the Total profit for the above model
Exercise - 8
Using arena, determine the value of double integral


3
1
4
2
2
3
9 dydx
y
x
I
Find the approximations of integral I for N = 50 N, 100 ,10000
Exercise – 9
Two Types of Parts arrive at a four-machine system according to an exponential distribution
with a mean of 25 minutes. 80% of the parts entering the system are Part A’s and the
remaining 20% are Part B’s.
Parts move through the following stations with the processing times shown below. All
processing times are in minutes and follow the triangular distribution.
Type A
Machine 1 (18, 19, 20 )
Machine 2 (18, 20, 22 )
Machine 4 (18, 20, 22 )
Type B
Machine 3 (15, 20, 25 )
Machine 2 (25, 26, 27 )
Machine 4 (20, 30, 31 )
After completing processing, all parts exit the system at the warehouse.

Movements between part arrival and machines 1 and 3 are unconstrained and take 2 minutes.
Machine 2 is unconstrained and takes 2 minutes.
Parts are transported between machine 2 and machine 4 and between machine 4 and the ware
house by a single forklift. The forklift moves at a velocity of 15 feet per minute. After every 4
hours of operation, the forklift goes down for 15 minutes to change its battery at the
warehouse.
Distances are as follows:
From To Distance (Feet)
Machine 2 Machine 4 75
Machine 4 Ware house 60
Run the simulation for 80 hours and collect statistics on the machine utilization, part flow
time and Transporter utilization.
Exercise - 10
Two Types of Parts arrive at a four-machine system according to an exponential distribution
with a mean of 25 minutes. 80% of the parts entering the system are Part A’s and the
remaining 20% are Part B’s.
Parts move through the following stations with the processing times shown below. All
processing times are in minutes and follow the triangular distribution.
Type A
Machine 1 (18, 19, 20 )
Machine 2 (18, 20, 22 )
Machine 4 (18, 20, 22 )
Type B
Machine 3 (15, 20, 25 )
Machine 2 (25, 26, 27 )
Machine 4 (20, 30, 31 )

After completing processing, all parts exit the system at the warehouse.
Movements between part arrival and machines 1 and 3 are unconstrained and take 2 minutes.
Machine 2 is unconstrained and takes 2 minutes.
Parts are transported between machine 2 and machine 4 and between machine 4 and the ware
house by a Conveyor. The Conveyor moves at a velocity of 15 feet per minute.
Distances are as follows:
From To Distance (Feet)
Machine 2 Machine 4 75
Run the simulation for 80 hours and collect statistics on the machine utilization, part flow
time and Conveyor utilization.
Exercise - 11
A bank Lobby has four Tellers – Alice, Mary, Jeff and Doris – with similar working
characteristics. The customer arrival pattern varies over time. The average number of arrivals
per hour is 10, 20, 40, 36, 27, 32, 18, and 4 for each of the 8 one – hour period from the
opening of the bank lobby until the closing time. During each period, the arrival process is
poison. In addition, customers can arrive in groups of more than one. For each arrival
instance, there is a 75 percent probability that it is a single customer, a 20 percent probability
that that the group size consists of two customers, and a 5 percent probability that three
customers are in group.
The number of banking transactions for each customer is sampled from the distributions
which was obtained from historical data and are as given below:
Probability distribution of number of transaction per customer
Number of Transactions 1 2 3 4 5 6
Probability (%) 20 30 22 15 8 5
A single queue serves all four tellers. When a customer enters the lobby, she will join the
queue if the total number of customers in the lobby – that is, the number of customers being
served plus the number of customers waiting in the queue is less than 10. Otherwise, she will
balk. The service time for the customer depends on the number of transactions to be

processed for the customers. Processing time for each transaction has an erlang distribution
with a mean 1.08 minutes and the number of stages equal to 2.
Eight hours of the bank lobby operations will be simulated i.e. from 9 a.m to 5 p.m.
Exercise - 12
Customer arrives at a bank and enters a queue to wait for the teller. When a customer reaches
the teller, he performs his transaction. When the initial transaction is complete the teller
determines if the customer must see the Manager, if this is the case the customer moves to the
single Manager, when finished the customer returns to the teller queue to re do his
transaction. If the customer is not required to see the Manager he performs his transactions
and leaves the bank.
The inter arrival time for the customer is expo (5 min). the travel time from entrance to teller
and from teller to exit is both 1 min. All teller transaction time is are normally distributed
with mean of 3 min and std. deviation of 1min. 10% of the customers are required to see the
Manager. Travel time to and from the Manager takes 1.5 min and the Manager time follows a
triangular distribution (12, 15,20 mins.). Collect the statistics on the teller and Manager
utilization, customer flow time and the number in teller queue. Run the simulation for 8.5
hours.
(b) For the model, if the Manager takes a 15 min break in the morning, a 60 min lunch, and
a 15 min afternoon break. if he is meeting with a customer he will finish this activity and then
take a full break. The Manager also must attend to bank emergencies, according to the
experience of similar bank branch, the frequency of emergencies is about twice per day.
Required to deal with each emergency will be used for this analysis. if the emergencies occur
when the Manager is away from the office, he will deal with it when he returns. It is found
that the down time has the following expression, 5 + 15 * beta(1.15,1.13) and an uptime of
expo(255).run simulation for 8.5 hours/day.
Collect the statistics on above system. Run the simulation for 8.5 hours.
Exercise - 14
Parts arrive with a poison distribution with mean inter arrival time of 3.5 minutes. The
preparation process does not use any resources, has a delay that is uniformly distributed
between 1.5 and 3.5 minutes, and a cost allocated to non-value added category. The service
process uses one unit of the resource server. Its delay is uniformly distributed between 1.7

and 2.5 minutes, and a cost allocated to value added category. The simulation is run for 5000
minutes. Determine the total cost of the system.
The cost data’s are as follows:
i. Parts: ii. Resource
Holding Cost/hour Rs.132 Capacity 1
Initial Value added Cost Rs. 10.5 Bust / hour 360
Initial Non Value added Cost Rs. 3.1 Idle / hour 150
Initial Waiting Cost Rs. 1.6 Per usage 1.00
Initial Transfer Cost Rs. 4.2
Additional Exercises (Assignments)
Exercise – 15.
Mortgage applications are initiated in the corporation office, the time for initiation follows
exponential distribution with a mean of 2 hours, these application are reviewed by mortgage
review clerk, the processing of these applications follows triangular distribution with values
(1, 1.75, 3) hours, the mortgage review clerk thoroughly scrutinizes these applications and it
is found that about 88% of the applications are accepted and remaining applications are
rejected. The mortgage clerk has a busy cost of Rs.12 per hour and an idle cost of Rs. 12 per
hour. Run the simulation for 20 days and find the following:
On average, how long did mortgage applications spend in the modeled process?
What was the average cost of reviewing a mortgage application?
What was the longest time an application spent in review?
What was the maximum number of applications waiting for review?
What proportion of time was the Mortgage Review Clerk busy?
b) In the above model, a receptionist is employed for initial screening of applications as and
when the applications comes, it is found that receptionist follow triangular distribution with
values (15, 25, 45) minutes and found to be a non value added item in the system, the
receptionist has a busy cost of Rs. 6.75 per hour and an idle cost of Rs. 6.75 per hour. The
screened applications are then sent to Mortgage Review Clerk, the processing of these
applications follows triangular distribution with values (1, 1.75, 3) hours, the mortgage
review clerk thoroughly scrutinizes these applications and it is found that about 88% of the
applications are accepted and remaining applications are rejected. The mortgage clerk has a
busy cost of Rs.12 per hour and an idle cost of Rs. 12 per hour. Run the simulation for 20
days and find the following:

Run the simulation for 20 days and find the following:
On average, how long did mortgage applications spend in the modeled process?
What was the average cost of reviewing a mortgage application by receptionist and clerk?
What was the longest time an application spent in review?
What was the maximum number of applications waiting for review?
What proportion of time were the receptionist and Mortgage Review Clerk busy?
(c) Suppose the screening of application is done by receptionist and found that 92 % of
the applications are forwarded for review by mortgage clerk and the remaining are returned
for further corrections. The processing of applications follows the triangular distribution with
values (1, 1.75, 3) hours and also depends on the screening factor of 0.9 with respect to clerk.
the mortgage review clerk thoroughly scrutinizes these applications and it is found that about
94% of the applications are accepted and remaining applications are rejected. The mortgage
clerk has a busy cost of Rs.12 per hour and an idle cost of Rs. 12 per hour and the receptionist
has a busy cost of Rs. 6.75 per hour and an idle cost of Rs. 6.75 per hour.. Run the
simulation for 20 days and find the following:
Run the simulation for 20 days and find the following:
 On average, how long did mortgage applications spend in the modeled process?
 What was the average cost of reviewing a mortgage application by receptionist and
clerk?
 What was the longest time an application spent in review?
 What was the maximum number of applications waiting for review?
 What proportion of time were the receptionist and Mortgage Review Clerk busy?
Exercise – 16
A gasoline filling station consists of a fuel depot, which has two types of fuels – Regular and
Premium and a stream of truck arriving to take fuel. Trucks arrival forms a Poisson stream
with a mean time between the arrivals is of 20 minutes. The trucks fuel request is 65%
Regular fuel and remaining Premium fuel. The amount of fuel requested is uniformly
distributed between 10000 and 14000 gallons of both the fuels .Each pump can deliver fuel
30 gallons /second or 1800 gallons /minute.
Simulate the above system and make suitable assumptions if any.

Exercise – 17
People arrive at the barber shop at the rate of 1 every 4.5minutes. If a shop is full (it can hold
5people altogether). 30% of the potential customer leaves and come back in 60+/- 20
minutes. The other leaves and do not return .One barber gives a haircut in 8+/-2 minutes
whereas the second talks a lot and it takes 12+/-4 minutes. If both the barbers are idle, a
customer prefers the 1st
barber. Simulate the system until 300 customers have received the
haircut.
Exercise - 18
A Xerox center has one fast copier and one slow copier. The copy time per pager for the fast
copier is normally distributed with mean 1.6 seconds and standard deviation 0.3 seconds. The
copy time per page for the slow copier is normally distributed with mean 2.8 seconds and
standard deviation 0.6 seconds. The arrival process is Poisson, so the inter arrival time
distribution for customers is exponential, with mean 3.0 minutes. The number of copies
requested by each customer is uniformly distributed between 10 and 50 copies.
The policy for selecting a copier is as follows: If the number of copies requested is less than
or equal to 30, the slow copier will be used. If the number of copies exceeds 30, the fast
copier is used, with one exception: If no jobs are in progress on the slow copier and the
number of jobs waiting for the fast copier is at least two, then the customer will be served by
slow copier. After the customer gives the originals for copying, she should proceeds to the
service counter to pay for the copying. The time to complete the payment transaction is
normally distributed with mean 2.1 minutes and standard deviation 0.6 minutes. As soon as
both payment and the copying are finished, the customer takes the copies and departs the
copying centre. The copy center works 10 hours per day for 5 replications.
Management has requested the model to be developed because they concerned that customers
have to wait too long for copies. Recently, several customers complained about long
waiting’s. Their standard is that customers waiting time should average no more than 3
minutes. If the mean waiting time is too long, several options are available: The policy for
allocating jobs to the fast copier could be modified or the company could purchase an
additional copier which could be either a slow copier or a fast copier.

Viva Voce
1. Define Simulation? Explain its significance in Industrial Engineering.
2. Explain in detail classification of Simulation techniques
3. List advantages, disadvantages and application of Simulation.
4. Write a note on optimization techniques considered in Industrial Engineering
branch.
5. List and explain the necessary criteria for selection of simulation software.
6. List some of the commercially available simulation software’s and simulation
languages.
7. Explain the concept of Monte Carlo simulation, under what circumstance we use
this technique, and state its application.
8. Write a note on random numbers, its properties and methods of obtaining Random
number generators.
9. Write PDF, CDF, Characteristic curves and applications of distributions used in
Simulation modeling &analysis.
10. Explain the importance of P – Test, K-S test and Chi-square test in Statistical
distributions.
11. Write a note on use of MS Excel functions in relation to simulation analysis.
12. Write a note on Arena simulation software and its applications in Industries
13. List the advantages and disadvantages of Arena Package
14. List software & hardware specifications in IEM computer lab.
15. Write a brief layout of IEM computer lab.

SMA lab Manual JAN 2021.pdf

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