## 2.12 Exercises

**Exercise 2.1**

*True*or

*False*: The simulation clock for a discrete event dynamic stochastic model jumps in equal increments of time in the defined time units. For example, 1 second, 2 seconds, 3 seconds, etc.

**Exercise 2.2**

The \(\underline{\hspace{3cm}}\) of a system is defined to be that collection of variables necessary to describe the system at any time, relative to the objectives of study.

An \(\underline{\hspace{3cm}}\) is defined as an instantaneous occurrence that may change the state of the system.

An \(\underline{\hspace{3cm}}\) describes the properties of entities by data values.

**Exercise 2.3**Provide the missing concept or definition concerning an activity diagram.

Concept | Definition |
---|---|

(a) | Represented as a circle with a queue name labeled inside |

(b) | Shown as a rectangle with an appropriate label inside |

Resource | (c) |

Lines/arcs | (d) |

(e) | Indicates the creation or destruction of an entity |

**Exercise 2.4**The service times for a automated storage and retrieval system has a shifted exponential distribution. It is known that it takes a minimum of 15 seconds for any retrieval. The rate parameter of the exponential distribution is \(\lambda = 45\) retrievals per second. Setup a model that will generate 20 observations of the retrieval times. Report the minimum, maximum, sample average, and 95% confidence interval half-width of the observations.

**Exercise 2.5**The time to failure for a computer printer fan has a Weibull distribution with shape parameter \(\alpha = 2\) and scale parameter \(\beta = 3\). Setup a model that will generate 50 observations of the failure times. Report the minimum, maximum, sample average, and 95% confidence interval half-width of the observations.

**Exercise 2.6**The time to failure for a computer printer fan has a Weibull distribution with shape parameter \(\alpha = 2\) and scale parameter \(\beta = 3\). Testing has indicated that the distribution is limited to the range from 1.5 to 4.5. Set up a model to generate 100 observations from this truncated distribution. Report the minimum, maximum, sample average, and 95% confidence interval half-width of the observations.

**Exercise 2.7**The interest rate for a capital project is unknown. An accountant has estimated that the minimum interest rate will between 2% and 5% within the next year. The accountant believes that any interest rate in this range is equally likely. You are tasked with generating interest rates for a cash flow analysis of the project. Set up a model to generate 100 observations of the interest rate values for the capital project analysis. Report the minimum, maximum, sample average, and 95% confidence interval half-width of the observations.

**Exercise 2.8 **Develop a model to generate 30 observations from the following probability density
function:

**Exercise 2.9 **Suppose that the service time for a patient consists of two distributions. There
is a 25% chance that the service time is uniformly distributed with
minimum of 20 minutes and a maximum of 25 minutes, and a 75% chance that
the time is distributed according to a Weibull distribution with shape
of 2 and a scale of 4.5.

**Exercise 2.10 **Suppose that \(X\) is
a random variable with a \(N(\mu = 2, \sigma = 1.5)\) normal distribution.
Generate 100 observations of \(X\) using a simulation model.

Estimate the mean from your observations. Report a 95% confidence interval for your point estimate.

Estimate the variance from your observations. Report a 95% confidence interval for your point estimate.

Estimate the \(P(X>3)\) from your observations. Report a 95% confidence interval for your point estimate.**Exercise 2.11**Samples of 20 parts from a metal grinding process are selected every hour. Typically 2% of the parts need rework. Let \(X\) denote the number of parts in the sample of 20 that require rework. A process problem is suspected if \(X\) exceeds its mean by more than 3 standard deviations. Simulate 30 hours of the process, i.e. 30 samples of size 20, and estimate the chance that \(X\) exceeds its expected value by more than 3 standard deviations.

**Exercise 2.12**Consider the following discrete distribution of the random variable \(X\) whose probability mass function is \(p(x)\). Setup a model to generate 30 observations of the random variable \(X\). Report the minimum, maximum, sample average, and 95% confidence interval half-width of the observations.

\(x\) | 0 | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|

\(p(x)\) | 0.3 | 0.2 | 0.2 | 0.1 | 0.2 |

**Exercise 2.13**The demand for parts at a repair bench per day can be described by the following discrete probability mass function. Setup a model to generate 30 observations of the demand. Report the minimum, maximum, sample average, and 95% confidence interval half-width of the observations.

Demand | 0 | 1 | 2 |
---|---|---|---|

Probability | 0.3 | 0.2 | 0.5 |

**Exercise 2.14 **Using and the Monte Carlo method estimate the following integral with 95% confidence
to within \(\pm 0.01\).

**Exercise 2.15 **Using and the Monte Carlo method estimate the following integral with 99% confidence
to within \(\pm 0.01\).

\[\int\limits_{0}^{\pi} \left( \sin (x) - 8x^{2}\right) \mathrm{d}x\]

**Exercise 2.16 **Using and the
Monte Carlo method estimate the following integral with 99% confidence
to within \(\pm 0.01\).

\[\theta = \int\limits_{0}^{1} \int\limits_{0}^{1} \left( 4x^{2}y + y^{2}\right) \mathrm{d}x \mathrm{d}y\]

**Exercise 2.17**A firm is trying to decide whether or not it should purchase a new scale to check a package filling line in the plant. The scale would allow for better control over the filling operation and result in less overfilling. It is known for certain that the scale costs $800 initially. The annual cost has been estimated to be normally distributed with a mean of $100 and a standard deviation of $10. The extra savings associated with better control of the filling process has been estimated to be normally distributed with a mean of $300 and a standard deviation of $50. The salvage value has been estimated to be uniformly distributed between $90 and $100. The useful life of the scale varies according to the amount of usage of the scale. The manufacturing has estimated that the useful life can vary between 4 to 7 years with the chances given in the following table.

years | 4 | 5 | 6 | 7 |
---|---|---|---|---|

f(years) | 0.3 | 0.4 | 0.1 | 0.2 |

F(years) | 0.3 | 0.7 | 0.8 | 1.0 |

The interest rate has been varying recently and the firm is unsure of the rate for performing the analysis. To be safe, they have decided that the interest rate should be modeled as a beta random variable over the range from 6 to 9 percent with alpha = 5.0 and beta = 1.5. Given all the uncertain elements in the situation, they have decided to perform a simulation analysis in order to assess the expected present value of the decision and the chance that the decision has a negative return.

We desire to be 95% confident that our estimate of the true expected present value is within \(\pm\) 10 dollars. Develop a model for this situation.

**Exercise 2.18**A firm is trying to decide whether or not to invest in two proposals A and B that have the net cash flows shown in the following table, where \(N(\mu, \sigma)\) represents that the cash flow value comes from a normal distribution with the provided mean and standard deviation.

End of Year | 0 | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|

A | \(N(-250, 10)\) | \(N(75, 10)\) | \(N(75, 10)\) | \(N(175, 20)\) | \(N(150, 40)\) |

B | \(N(-250, 5)\) | \(N(150, 10)\) | \(N(150, 10)\) | \(N(75, 20)\) | \(N(75, 30)\) |

The interest rate has been varying recently and the firm is unsure of the rate for performing the analysis. To be safe, they have decided that the interest rate should be modeled as a beta random variable over the range from 2 to 7 percent with \(\alpha_1 = 4.0\) and \(\alpha_2 = 1.2\). Given all the uncertain elements in the situation, they have decided to perform a simulation analysis in order to assess the situation. Use to answer the following questions:

Compare the expected present worth of the two alternatives. Estimate the probability that alternative A has a higher present worth than alternative B.

Determine the number of samples needed to be 95% confidence that you have estimated the \(P[PW(A) > PW(B)]\) to within \(\pm\) 0.10.

**Exercise 2.19 **A U-shaped component is to be formed from
the three parts A, B, and C. The picture is shown in the figure below.
The length of A is lognormally distributed with a mean of 20 millimeters
and a standard deviation of 0.2 millimeter. The thickness of parts B and
C is uniformly distributed with a minimum of 4.98 millimeters and a
maximum of 5.02 millimeters. Assume all dimensions are independent.

Develop a model to estimate the probability that the gap \(D\) is less than 10.1 millimeters with 95% confidence to within plus or minus 0.01.

**Exercise 2.20**Shipments can be transported by rail or trucks between New York and Los Angeles. Both modes of transport go through the city of St. Louis. The mean travel time and standard deviations between the major cities for each mode of transportation are shown in the following figure.

Assume that the travel times (in either direction) are lognormally distributed as shown in the figure. For example, the time from NY to St. Louis (or St. Louis to NY) by truck is 30 hours with a standard deviation of 6 hours. In addition, assume that the transfer time in hours in St. Louis is triangularly distributed with parameters (8, 10, 12) for trucks (truck to truck). The transfer time in hours involving rail is triangularly distributed with parameters (13, 15, 17) for rail (rail to rail, rail to truck, truck to rail). We are interested in determining the shortest total shipment time combination from NY to LA. Develop an simulation for this problem.

How many shipment combinations are there?

Write an expression for the total shipment time of the truck only combination.

We are interested in estimating the average shipment time for each shipment combination and the probability that the shipment combination will be able to deliver the shipment within 85 hours.

Estimate the probability that a shipping combination will be the shortest.

**Exercise 2.21 **A firm produces YBox
gaming stations for the consumer market. Their profit function is:
\[\text{Profit} = (\text{unit price} - \text{unit cost})\times(\text{quantity sold}) - \text{fixed costs}\]

Unit Cost | 80 | 90 | 100 | 110 |
---|---|---|---|---|

Probability | 0.20 | 0.40 | 0.30 | 0.10 |

Quantity Sold | 1000 | 2000 | 3000 | |

Probability | 0.10 | 0.60 | 0.30 | |

Fixed Cost | 50000 | 65000 | 80000 | |

Probability | 0.40 | 0.30 | 0.30 |

Use a simulation model to generate 1000 observations of the profit.

Estimate the mean profit from your sample and compute a 95% confidence interval for the mean profit. Estimate the probability that the profit will be positive.

**Exercise 2.22 **T. Wilson operates a sports magazine stand before each game. He can buy
each magazine for 33 cents and can sell each magazine for 50 cents.
Magazines not sold at the end of the game are sold for scrap for 5 cents
each. Magazines can only be purchased in bundles of 10. Thus, he can buy
10, 20, and so on magazines prior to the game to stock his stand. The
lost revenue for not meeting demand is 17 cents for each magazine
demanded that could not be provided. Mr. Wilson’s profit is as follows:

\[\begin{aligned} \text{Profit} & = (\text{revenue from sales}) - (\text{cost of magazines}) \\ & - (\text{lost profit from excess demand}) \\ & + (\text{salvage value from sale of scrap magazines}) \end{aligned} \]

Not all game days are the same in terms of potential demand. The type of day depends on a number of factors including the current standings, the opponent, and whether or not there are other special events planned for the game day weekend. There are three types of game days demand: high, medium, low. The type of day has a probability distribution associated with it.

Type of Day | High | Medium | Low |
---|---|---|---|

Probability | 0.35 | 0.45 | 0.20 |

The amount of demand for magazines then depends on the type of day according to the following distributions:

Demand | PMF | CDF | PMF | CDF | PMF | CDF |

40 | 0.03 | 0.03 | 0.1 | 0.1 | 0.44 | 0.44 |

50 | 0.05 | 0.08 | 0.18 | 0.28 | 0.22 | 0.66 |

60 | 0.15 | 0.23 | 0.4 | 0.68 | 0.16 | 0.82 |

70 | 0.2 | 0.43 | 0.2 | 0.88 | 0.12 | 0.94 |

80 | 0.35 | 0.78 | 0.08 | 0.96 | 0.06 | 1.0 |

90 | 0.15 | 0.93 | 0.04 | 1.0 | ||

100 | 0.07 | 1.0 |

Let \(Q\) be the number of units of magazines purchased (quantity on hand) to setup the stand. Let \(D\) represent the demand for the game day. If \(D > Q\), Mr. Wilson sells only \(Q\) and will have lost sales of \(D-Q\). If \(D < Q\), Mr. Wilson sells only \(D\) and will have scrap of \(Q-D\). Assume that he has determined that \(Q = 50\).

Make sure that you can estimate the average profit and the probability that the profit is greater than zero for Mr. Wilson. Develop a model to estimate the average profit based on 100 observations.

**Exercise 2.23 **The time for an automated storage and retrieval system in a warehouse to
locate a part consists of three movements. Let \(X\) be the time to travel
to the correct aisle. Let \(Y\) be the time to travel to the correct
location along the aisle. And let \(Z\) be the time to travel up to the
correct location on the shelves. Assume that the distributions of \(X\),
\(Y\), and \(Z\) are as follows:

\(X \sim\) lognormal with mean 20 and standard deviation 10 seconds

\(Y \sim\) uniform with minimum 10 and maximum 15 seconds

\(Z \sim\) uniform with minimum of 5 and a maximum of 10 seconds

Develop a model that can estimate the average total time that it takes to locate a part and can estimate the probability that the time to locate a part exceeds 60 seconds. Base your analysis on 1000 observations.

**Exercise 2.24**Lead-time demand may occur in an inventory system when the lead-time is other than instantaneous. The lead-time is the time from the placement of an order until the order is received. The lead-time is a random variable. During the lead-time, demand also occurs at random. Lead-time demand is thus a random variable defined as the sum of the demands during the lead-time, or LDT = \(\sum_{i=1}^T D_i\) where \(i\) is the time period of the lead-time and \(T\) is the lead-time. The distribution of lead-time demand is determined by simulating many cycles of lead-time and the demands that occur during the lead-time to get many realizations of the random variable LDT. Notice that LDT is the

*convolution*of a random number of random demands. Suppose that the daily demand for an item is given by the following probability mass function:

Daily Demand (items) | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|

Probability | 0.10 | 0.30 | 0.35 | 0.10 | 0.15 |

The lead-time is the number of days from placing an order until the firm receives the order from the supplier.

Assume that the lead-time is a constant 10 days. Develop a model to simulate 1000 instances of LDT. Report the summary statistics for the 1000 observations. Estimate the chance that LDT is greater than or equal to 10. Report a 95% confidence interval on your estimate.

Assume that the lead-time has a shifted geometric distribution with probability parameter equal to 0.2 Use a model to simulate 1000 instances of LDT. Report the summary statistics for the 1000 observations. Estimate the chance that LDT is greater than or equal to 10. Report a 95% confidence interval on your estimate.

**Exercise 2.25 **If \(Z \sim N(0,1)\), and \(Y = \sum_{i=1}^k Z_i^2\) then \(Y \sim \chi_k^2\),
where \(\chi_k^2\) is a chi-squared random variable with \(k\) degrees of
freedom. Setup a model to generate 50 \(\chi_5^2\) random variates.
Report the minimum, maximum, sample average, and 95% confidence interval
half-width of the observations.

**Exercise 2.26 **Setup a model that
will generate 30 observations from the following probability density
function using the Acceptance-Rejection algorithm for generating random
variates.

\[f(x) = \begin{cases} \dfrac{3x^2}{2} & -1 \leq x \leq 1\\ 0 & \text{otherwise} \\ \end{cases}\]

**Exercise 2.27 **This procedure is due to (Box and Muller 1958). Let \(U_1\) and \(U_2\) be two independent uniform (0,1) random variables and define:

\[\begin{aligned} X_1 & = \cos (2 \pi U_2) \sqrt{-2 ln(U_1)}\\ X_2 & = \sin (2 \pi U_2) \sqrt{-2 ln(U_1)}\end{aligned}\]

It can be shown that \(X_1\) and \(X_2\) will be independent standard normal random variables, i.e. \(N(0,1)\). Use to implement the Box and Muller algorithm for generating normal random variables. Generate 1000 \(N(\mu = 2, \sigma = 0.75)\) random variables via this method. Report the minimum, maximum, sample average, and 95% confidence interval half-width of the observations.

**Exercise 2.28 **Using the supplied
data set, draw the sample path for the state variable, \(Y(t)\). Assume
that the value of \(Y(t)\) is the value of the state variable just after
time \(t\). Compute the time average over the supplied time range.

\(t\) | 0 | 1 | 6 | 10 | 15 | 18 | 20 | 25 | 30 | 34 | 39 | 42 |

\(Y(t)\) | 1 | 2 | 1 | 1 | 1 | 2 | 2 | 3 | 2 | 1 | 0 | 1 |

**Exercise 2.29 **Using the supplied data set, draw the sample path for the state
variable, \(N(t)\). Give a formula for estimating the time average number
in the system, \(N(t)\), and then use the data to compute the time average
number in the system over the range from 0 to 25. Assume that the value
of \(N(t\) is the value of the state variable just after time \(t\).

\(t\) | 0 | 2 | 4 | 5 | 7 | 10 | 12 | 15 | 20 |

\(N(t)\) | 0 | 1 | 0 | 1 | 2 | 3 | 2 | 1 | 0 |

**Exercise 2.30**Consider the banking situation described within the chapter. A simulation analyst observed the operation of the bank and recorded the information given in the following table. The information was recorded right after the bank opened during a period of time for which there was only one teller working. From this information, you would like to re-create the operation of the system.

Customer | Time of | Service |

Number | Arrival | Time |

1 | 3 | 4 |

2 | 11 | 4 |

3 | 13 | 4 |

4 | 14 | 3 |

5 | 17 | 2 |

6 | 19 | 4 |

7 | 21 | 3 |

8 | 27 | 2 |

9 | 32 | 2 |

10 | 35 | 4 |

11 | 38 | 3 |

12 | 45 | 2 |

13 | 50 | 3 |

14 | 53 | 4 |

15 | 55 | 4 |

Complete a table similar to that used in the chapter and compute the average of the system times for the customers. What percentage of the total time was the teller idle? Compute the percentage of time that there were 0, 1, 2, and 3 customers in the queue.

**Exercise 2.31**Consider the following inter-arrival and service times for the first 25 customers to a single server queuing system.

Customer | Inter-Arrival | Service | Time of |

Number | Time | Time | Arrival |

1 | 22 | 74 | 22 |

2 | 89 | 105 | 111 |

3 | 21 | 34 | 132 |

4 | 26 | 38 | 158 |

5 | 80 | 23 | |

6 | 81 | 26 | |

7 | 78 | 90 | |

8 | 20 | 26 | |

9 | 32 | 37 | |

10 | 13 | 88 | |

11 | 28 | 38 | |

12 | 18 | 73 | |

13 | 29 | 93 | |

14 | 19 | 25 | |

15 | 20 | 93 | |

16 | 23 | 5 | |

17 | 78 | 37 | |

18 | 20 | 51 | |

19 | 109 | 28 | |

20 | 78 | 85 |

We are given the inter-arrival times. Determine the time of arrival of each customer. Complete the event and state variable change table associated with this situation. Draw a sample path graph for the variable \(N(t)\) which represents the number of customers in the system at any time \(t\). Compute the average number of customers in the system over the time period from 0 to 700. Draw a sample path graph for the variable \(NQ(t)\) which represents the number of customers waiting for the server at any time \(t\). Compute the average number of customers in the queue over the time period from 0 to 700. Draw a sample path graph for the variable \(B(t)\) which represents the number of servers busy at any time t. Compute the average number of busy servers in the system over the time period from 0 to 700. Compute the average time spent in the system for the customers.

**Exercise 2.32 **Parts arrive at a station with a single machine according to a Poisson
process with the rate of 1.5 per minute. The time it takes to process
the part has an exponential distribution with a mean of 30 second. There
is no upper limit on the number of parts that wait for process. Setup an
model to estimate the expected number of parts waiting in the queue and
the utilization of the machine. Run your model for 10000 seconds for 30
replications and report the results. Use M/M/1 queueing results from the
chatper to verify that your simulation is working as intended.

**Exercise 2.33 **A large car dealer has a policy of providing cars for its customers that
have car problems. When a customer brings the car in for repair, that
customer has use of a dealer’s car. The dealer estimates that the dealer
cost for providing the service is $10 per day for as long as the
customer’s car is in the shop. Thus, if the customer’s car was in the
shop for 1.5 days, the dealer’s cost would be $15. Arrivals to the shop
of customers with car problems form a Poisson process with a mean rate
of one every other day. There is one mechanic dedicated to the
customer’s car. The time that the mechanic spends on a car can be
described by an exponential distribution with a mean of 1.6 days. Setup
a model to estimate the expected time within the shop for the cars and
the utilization of the mechanic. Run your model for 10000 days for 30
replications and report the results. Estimate the total cost per day to
the dealer for this policy. Use the M/M/1 queueing results from the chapter
to verify that your simulation is working as intended.

**Exercise 2.34 **YBox video game players arrive according to a Poisson process with rate
10 per hour to a two-person station for inspection. The inspection time
per YBox set is EXPO(10) minutes. On the average 82% of the sets pass
inspection. The remaining 18% are routed to an adjustment station with a
single operator. Adjustment time per YBox is UNIF(7,14) minutes. After
adjustments are made, the units depart the system. The company is
interested in the total time spent in the system. Run your model for
10000 minutes for 30 replications and report the results.

**Exercise 2.35**Referring to the pharmacy model discussed in Section 2.6.1, suppose that the customers arriving to the drive through pharmacy can decide to enter the store instead of entering the drive through lane. Assume a 90% chance that the arriving customer decides to use the drive through pharmacy and a 10% chance that the customer decides to use the store. Model this situation with and discuss the effect on the performance of the drive through lane. Run your model for 1 year, with 20 replications.

**Exercise 2.36 **The lack of memory property of the exponential distribution states that given
\(\Delta t\) is the time period that elapsed since the occurrence of the
last event, the time \(t\) remaining until the occurrence of the next
event is independent of \(\Delta t\). This implies that,
\(P \lbrace X > \Delta t + t|X > t \rbrace = P \lbrace X > t \rbrace\).
Describe a simulation experiment that would allow you to test the lack
of memory property empirically. Implement your simulation experiment in
and test the lack of memory property empirically. Explain in your own
words what lack of memory means.

**Exercise 2.37 **SQL queries arrive to a
database server according to a Poisson process with a rate of 1 query
every minute. The time that it takes to execute the query on the server
is typically between 0.6 and 0.8 minutes uniformly distributed. The
server can only execute 1 query at a time. Develop a simulation model to
estimate the average delay time for a query

### References

*Annals of Mathematical Statistics*29: 610–11.