STUPIDSID
1 (a)
Briefly explain the advantages and disadvantages of simulation.
10 M
1 (b)
What is simulation? Explain with flow-chart, the steps involved in simulation study.
10 M
2 (a)
A grocery store has one checkout counter. Customer arrive at this checkout counter at random from 1 to 8 minutes with probabilities as given below:
Assume that first customer arrives at time 0, Depict the simulation in tabular form.
| Service Time | 1 | 2 | 3 | 4 | 5 | 6 |
| Probability | 0.10 | 0.20 | 0.30 | 0.25 | 0.10 | 0.05 |
Simulate the arrival of 10 customer and calculate:
i) Average waiting time for a customer
ii) Probability that a customer has to wait
iii) Probability of a server being idle
iv) Average service time
v) Average time between arrivals.
| RD's for | 913 | 727 | 015 | 948 | 309 | 922 | 753 | 235 | 302 | |
| RD's for service time: | 84 | 10 | 74 | 53 | 17 | 79 | 91 | 67 | 89 | 38 |
Assume that first customer arrives at time 0, Depict the simulation in tabular form.
14 M
2 (b)
Explain event scheduling algorithm by generating system snapshot at clock=t and clock t1.
6 M
3 (a)
What is list processing? Explain the basic operations of list processing.
8 M
3 (b)
What is poison process? List out the assumptions which are needed to fulfil the counting process, {N(t), t>0}, is said to be Poisson process with mean rate λ.
6 M
3 (c)
With example explain the properties of Poisson process.
6 M
4 (a)
Explain the characteristics of a queuing system. List different queuing notations.
12 M
4 (b)
Explain the various steady state parameters of M/G/I queue.
8 M
5 (a)
Use linear congruential method to generate a sequence of 5 random members, with x0=27, c=43, a=17, m=100.
4 M
5 (b)
Use the K-S table with α=0.05 for the following set of random members. Determine if the hypothesis that the number are uniformly distributed in the interval (0, 1). Random members are: 0.54, 0.73, 0.98, 0.11, 0.68.
8 M
5 (c)
Test whether the 2nd, 9th, 16th, ....... etc/so on numbers in the following sequence are auto correlated by taking α=0.05.
| 0.38 | 0.48 | 0.36 | 0.01 | 0.54 | 0.34 | 0.96 | 0.06 | 0.61 | 0.85 |
| 0.48 | 0.86 | 0.14 | 0.86 | 0.89 | 0.37 | 0.49 | 0.60 | 0.04 | 0.83 |
| 0.42 | 0.83 | 0.37 | 0.21 | 0.90 | 0.89 | 0.91 | 0.79 | 0.77 | 0.99 |
| 0.95 | 0.27 | 0.41 | 0.81 | 0.96 | 0.31 | 0.09 | 0.06 | 0.23 | 0.77 |
| 0.73 | 0.47 | 0.13 | 0.55 | 0.11 | 0.75 | 0.36 | 0.25 | 0.23 | 0.72 |
| 0.60 | 0.84 | 0.70 | 0.30 | 0.26 | 0.38 | 0.05 | 0.19 | 0.73 | 0.44 |
8 M
6 (a)
Explain acceptance-rejection technique for Poisson distribution. Generate 5 Poisson variates with mean α=0.25. Random numbers are: 0.073, 0.693, 0.945, 0.739, 0.014, 0.342.
10 M
6 (b)
Test whether the following data follows Poisson distribution using the chi-square test of goodness of fit. With mean α=0.05.
| Arrivals / Period | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| Frequency | 12 | 10 | 19 | 17 | 10 | 8 | 7 | 5 | 5 | 3 | 3 | 1 |
10 M
7 (a)
The following data are available on the processing time at a m/c (in minutes). Develop an input model for the processing time: 0.64, 0.59, 1.1, 3.3, 0.54, 0.04, 0.45, 0.25, 4.4, 2.7, 2.4, 1.1, 3.6, 0.61, 0.20, 1.0, 0.27, 1.7, 0.04, 0.34.
8 M
7 (b)
Explain types of simulations with respect to output analysis. Briefly explain the confidence- interval estimation method.
12 M
8 (a)
Explain the components of verification and validation process. Explain with neat diagram, model building, verification and validation process.
12 M
8 (b)
With neat diagram, explain the iterative process of calibrating a model.
8 M
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