STUPIDSID
1 (a)
List any five circumstances, when the simulation is the appropriate tool and when it is not.
10 M
1 (b)
Explain the steps in simulation study, with the flow chart.
10 M
2 (a)
Explain the following:
i) System
ii) Event list
iii) Entity
iv) Event.
i) System
ii) Event list
iii) Entity
iv) Event.
4 M
Write the flow chart with respect to single channel queue:
2 (b) (i)
Execution of the arrival event.
3 M
2 (b) (ii)
Execution of the departure event.
3 M
2 (c)
One company uses 6 trucks of haul manganese ore from kolar to its industry. There are two loaders, to load each truck. After loading, a truck moves to the weighing scale to be weighted. The queue discipline is FIFO. When it is weighed, a truck travels to the industry and returns to the loader queue. The distribution of loading time, weighing time and travel time are as follows:
Depict the simulation table and estimate the loader and scale utilization. Assume 5 trucks are at the loaders and one is at the scale, at time'0'. Stopping time TE=76 min.
Depict the simulation table and estimate the loader and scale utilization. Assume 5 trucks are at the loaders and one is at the scale, at time'0'. Stopping time TE=76 min.
| Loading time: | 10 | 5 | 5 | 10 | 15 | 10 | 10 |
| Weigh time: | 12 | 12 | 12 | 16 | 12 | 16 | |
| Travel time: | 60 | 100 | 40 | 40 | 80 |
10 M
3 (a)
Explain discrete random variable and continuous random variable with example.
8 M
3 (b)
Explain the following discrete distribution:
i) Binomial distribution
ii) Poisson distribution
i) Binomial distribution
ii) Poisson distribution
6 M
3 (c)
Explain the following continuous distribution:
i) Uniform distribution
ii) Exponential distribution.
i) Uniform distribution
ii) Exponential distribution.
6 M
4 (a)
Explain queue behaviour and queue discipline and list queuing notation for parallel server systems.
12 M
4 (b)
What is network of queue? Mention the general assumption for a stable system with infinite calling population.
8 M
5 (a)
Explain combined linear congruential generator.
6 M
5 (b)
Explain inverse-transform technique of producing random variates for
i) Exponential distribution
ii) Weibull distribution
i) Exponential distribution
ii) Weibull distribution
8 M
5 (c)
Generate three Poisson variates with mean α=0.2.
[Random number: 0.4357, 0.4146, 0.8353, 0.9952, 0.8004].
[Random number: 0.4357, 0.4146, 0.8353, 0.9952, 0.8004].
6 M
6 (a)
The sequence of numbers 0.44, 0.81, 0.14, 0.05, 0.93 has been generated. Use the Kolmogonov-Smirnov test with α=0.05 to determine if the hypothesis that the numbers are uniformly distributed in the interval [0, 1] can be rejected. Compare F(x) and SN(x) on a graph. [N=5, D0.05'=0.565].
10 M
6 (b)
Explain chi-square goodness of fit test. Apply it to Poisson assumption with α=3.64. Data size=100 and observed frequency
Oi=12, 10, 19, 17, 10, 8, 7, 5, 5, 3, 3, 1] [x20.05.5=111].
Oi=12, 10, 19, 17, 10, 8, 7, 5, 5, 3, 3, 1] [x20.05.5=111].
10 M
7 (a)
What are pseudo random numbers? What are the problems that occur while generating pseudo random number?
6 M
7 (b)
Enlist the steps involved in development of a useful model of input data and number of ways to select input models without data.
8 M
7 (c)
List any 6 suggested estimators for distributions often used in simulation.
6 M
8 (a)
Explain with a neat diagram, model building, verification and validation.
10 M
8 (b)
Explain the iterative process of calibrating a model.
10 M
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