Unit 1: Introduction to Simulation
1. Difference between static physical and dynamic physical models.
Solutoin
2. Describe different phases of simulation study with help of flowchart.
Solution
Phases of Simulation Study
A simulation study is carried out in four main phases consisting of several steps.
1. Phase I: Problem Formulation (Discovery Phase)
Define the problem clearly
Set objectives and project plan
Decide whether simulation is appropriate
2. Phase II: Model Building and Data Collection
Model conceptualization (abstract system)
Data collection
Model translation (coding)
Verification (check correctness of program)
Validation (check model with real system)
3. Phase III: Running the Model
Experimental design
Production runs
Output analysis
More runs if required
4. Phase IV: Implementation
Documentation and reporting
Implementation of results
3. What are the application areas of simulation?
Solution
Simulation is the process of designing a model of a real system and conducting experiments with it for the purpose of analysis, design, optimization, and training.
Application Areas of Simulation
Industrial and Business: Used for manufacturing system design, inventory control, supply chain management, and business decision-making.
Healthcare: Used for hospital management, patient flow analysis, medical facility planning, and disease/epidemic modeling.
Transportation: Used for traffic control, road network design, railway/airline scheduling, and airport logistics planning.
Engineering: Used in civil engineering for bridge, building, and earthquake simulation, as well as system design and performance testing.
Computer and Communication Systems: Used for network design, performance evaluation, operating system testing, and internet communication systems.
Military and Defense: Used for war strategy simulation, mission planning, operator training, and weapon system testing.
Environmental: Used for pollution control analysis, waste management systems, and environmental impact studies.
Training and Education: Used in flight simulators, operator training systems, and virtual learning environments to train personnel safely.
Scientific Research: Used for biological system modeling, population studies, physics simulations, and queuing system analysis.
4. What is a system modeling? What are the steps involved in system modeling.
Solution
System Modeling
System modeling is the process of creating a simplified representation of a real-world system to understand its behavior and predict its performance under different conditions.
Steps Involved in System Modeling
1. Phase I: Problem Formulation (Discovery Phase)
Define the problem clearly
Set objectives and project plan
Decide whether simulation is appropriate
2. Phase II: Model Building and Data Collection
Model conceptualization (abstract system)
Data collection
Model translation (coding)
Verification (check correctness of program)
Validation (check model with real system)
3. Phase III: Running the Model
Experimental design
Production runs
Output analysis
More runs if required
4. Phase IV: Implementation
Documentation and reporting
Implementation of results
5. Why model of a system is built? What is static model? Differentiate between static and dynamic mathematical models in simulation.
solution
A model of a system is built to study and analyze a system without using the real system.
Reasons:
To analyze systems before implementation and reduce design errors
To save cost and time compared to real experimentation
To study systems that are complex or not directly observable
To predict the effect of changes without disturbing the real system
To provide training environments (e.g., simulators)
To understand relationships between system components
Static Model
A static model is a model that represents a system at a particular point in time and does not consider changes over time.
Features:
Time-independent
Shows system in equilibrium/state
Simple and less flexible
Does not show system behavior over time
Example:
Architectural model of a building
Snapshot of a market at a fixed price
6. Differentiate between discrete and continuous system.
Solution
7. Discuss the merits and demerits of system simulation.
Solution
Merits (Advantages)
Allows safe experimentation without affecting real system
Saves cost and time in system design and testing
Helps in understanding complex systems
Useful for “what-if” analysis
Can identify bottlenecks and inefficiencies
Time can be compressed or expanded
Suitable for dangerous or real-time systems
Helps in decision making and planning
Demerits (Disadvantages)
Requires skilled personnel and training
Model building is time-consuming and costly
Results may be difficult to interpret
Output may be affected by randomness
Models may not be accurate representations
Not suitable when analytical solution is easier
Requires large amount of data
8. What do you understand by static mathematical model? Explain with example. Differentiate between stochastic and deterministic activities.
Solution
A static mathematical model is a model that represents a system using mathematical equations at a specific point in time, without considering any changes over time.
It is time-independent (no past or future behavior).
Describes the system in a state of equilibrium.
Does not show how variables change, only their final relationship.
Focuses on structure rather than behavior.
9. Describe the phases in simulation.
Solution
Phases in Simulation
The simulation process is divided into four main phases:
1. Phase I: Problem Formulation (Discovery Phase)
Define the problem clearly
Set objectives and project plan
Decide whether simulation is appropriate
2. Phase II: Model Building and Data Collection
Model conceptualization (abstract system)
Data collection
Model translation (coding)
Verification (check correctness of program)
Validation (check model with real system)
3. Phase III: Running the Model
Experimental design
Production runs
Output analysis
More runs if required
4. Phase IV: Implementation
Documentation and reporting
Implementation of results
Conclusion
Simulation is an iterative process, meaning steps may be repeated until satisfactory results are obtained.
10. Write short notes on:
a. System and its environment
System:
A system is a collection of interrelated components that work together to achieve a common objective.
Components of a System:
Entities: Objects of interest in the system
Attributes: Properties of entities
Activities: Processes that cause changes in the system
State of System:
The state is the condition of the system at a specific time, described by its variables.
b. System Environment
The system environment consists of all external factors that affect the system but are not part of it.
A boundary separates the system from its environment
Environment influences system behavior
Types of Activities:
Endogenous: Occur inside the system
Exogenous: Occur outside but affect the system
Types of Systems:
Open System: Interacts with environment
Closed System: No interaction (rare in real life)
Unit 2: Simulation of Continuous and Discrete System
1. What is analog computer? Explain with suitable example.
Solution
Analog Computer
An analog computer is a computing device that uses continuous physical quantities (such as voltage, current, or mechanical motion) to represent and solve mathematical problems.
-Works with continuous data
-Used to solve differential equations
-Provides approximate results
-Faster for real-time simulation of physical systems
Example: Electrical Circuit Analogy
An electrical circuit can be used to simulate a mechanical system.
Components like:
Resistance (R) → represents damping
Inductance (L) → represents mass
Capacitance (C) → represents elasticity
By adjusting these components, the behavior of a real system can be studied.
2. Differentiate between analog and digital computer.
Solution
3. Why accuracy of analog computer is low? Explain analog computer with suitable example.
Solution
Analog Computer:-
An analog computer is a device that performs computation using continuous physical quantities such as voltage, current, or mechanical motion.
Why Accuracy of Analog Computer is Low
Limited Precision in Measurement:
Physical quantities like voltage cannot be measured exactly, leading to errors.
Noise and Disturbances:
Electrical noise affects signals and reduces accuracy.
Component Imperfections:
Devices like resistors and capacitors have tolerances and are not ideal.
Approximate Nature:
Analog computers give approximate results, not exact values.
Example: Electrical Circuit Analogy
An RLC circuit is used to simulate a mechanical system.
Components represent:
Inductance (L) → Mass
Resistance (R) → Damping
Capacitance (C) → Spring
By analyzing voltage changes, system behavior can be studied.
4. Explain Monte Carlo simulation.
Solution
Monte Carlo Simulation:
Monte Carlo simulation is a technique that uses random numbers and repeated sampling to simulate and analyze the behavior of a system with uncertainty.
It is based on probability and randomness
Used when systems are too complex for analytical solutions
Produces approximate results through multiple trials
Steps in Monte Carlo Simulation:
Define the problem and model
Identify probability distributions of input variables
Generate random numbers
Perform simulation trials
Analyze the results
Example:
Suppose we want to estimate the probability of getting heads in coin tosses.
We simulate many random coin tosses using random numbers.
By repeating this many times, we estimate the probability.
5. Explain the concept of discrete event simulation. Explain poisson’s arrival pattern.
Solution
Discrete Event Simulation:
Discrete Event Simulation (DES) is a simulation technique in which the state of a system changes only at specific discrete points in time due to the occurrence of events.
State changes occur only at event times
Between events, the system state remains constant
Events include arrival, departure, service completion, etc.
Widely used in systems like banks, queues, networks
Example:
In a bank system, the number of customers changes only when:
A customer arrives
A customer leaves after service
Poisson Arrival Pattern:
A Poisson arrival pattern describes a process where arrivals occur randomly and independently over time.
Key Characteristics:
Arrivals occur one at a time
Arrivals are independent
Average arrival rate (λ) is constant
Used to model random events like customer arrivals
6. Write short notes on:
a. Feedback system
b. Non-Stationary Poission process
c. Digital analog simulator
a. Feedback System
Definition:
A feedback system is a system in which the output is fed back into the input to control or regulate the system.
Types:
Positive Feedback: Enhances or increases output
Negative Feedback: Reduces or stabilizes output
Example:
Thermostat in AC → maintains room temperature by using feedback
b. Non-Stationary Poisson Process
Definition:
A Non-Stationary Poisson Process is a process in which the arrival rate (λ) changes over time.
Key Points:
Arrival rate is not constant
Depends on time (rush hours, peak periods, etc.)
Violates stationary increment property
Used in systems with varying traffic like networks or customer flow
c. Digital Analog Simulator
Definition:
A digital-analog simulator is a digital system or software that simulates the behavior of an analog computer to solve continuous mathematical problems.
Key Features:
Uses digital computers to simulate continuous systems
Performs operations like integration, addition, and differentiation
More accurate and flexible than physical analog computers
Example:
Simulation of a mechanical or electrical system using software like simulation tools (e.g., MATLAB/Simulink)
Unit 3: Queuing System
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What is queuing system? Explain the elements of queuing system with example.
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Define traffic intensity and server utilization. Write down the Kendall’s notation for queuing system with example.
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Define queuing system. Explain different queuing disciplines. Also explain different performance measures for evaluation of queuing system.
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Define queuing system. Explain the Kendall’s notation for queuing system? What are the various performance measures in single server queuing system?
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Explain basic characteristics of queueing system.
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What is calling population? Explain arrival and service process in a queue.
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Applications of queuing system (implicit in numerical and theory questions).
Unit 4: Markov Chains
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Explain Markov chain with suitable example. What are different application areas of Markov chain?
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Explain Markov chain with suitable example.
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What is Markov chain? Explain with example.
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Explain Markov’s chain with a suitable example.
Unit 5: Random Numbers
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Explain the independence and uniformity property of random number. Perform test for independence using K-S test.
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What are the properties of random number? Use the Kolmogorov Smirnov test for uniformity.
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What are the two main properties of random numbers? Test auto-correlation.
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Generate 10 random integers using Linear congruential method.
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Use Mixed congruential method to generate a sequence of random numbers.
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Generate ten 3 digit random integers using Multiplicative Congruential method.
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Explain generation of non uniform random number using inverse method.
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Difference between chi-square test and KS test for uniformity.
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Define true random numbers and pseudo random numbers with its properties.
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Write short notes on:
a. Random variate
b. Poker test
c. Stationary poisson process
Unit 6: Verification and Validation
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Explain iterative process of calibrating a simulation model.
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Describe the process of model building, verification and validation in detail with example.
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What is three step approach for validation of simulation models?
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Define verification and validation. Explain the process of model verification in brief.
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Differentiate between validation and calibration. How can we perform validation of a model?
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Define the terms verification, calibration, validation and accreditation of models.
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“Building a model right” and “Building a right model”. Discuss the importance of V & V.
Unit 7: Analysis of Simulation Output
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Why is it necessary to analyze the simulation output? Explain different estimation methods used in simulation output analysis.
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Why confidence interval is needed in the analysis of simulation output? How can we establish a confidence interval?
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Explain different estimation methods used in simulation output analysis.
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What do you mean by replication of runs? Why it is necessary?
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Explain the importance of elimination of initial bias during simulation.
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Write short notes on:
a. Hypothesis testing
b. Simulation run statistics
Unit 8: Simulation of Computer Systems
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Develop GPSS block diagram and code for a manufacturing shop problem and explain blocks used.
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Why is GPSS called transaction flow oriented language?
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What is storage in GPSS? Describe the blocks associated to storage in GPSS.
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What is transaction in GPSS? Explain about facility in GPSS.
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Draw GPSS block diagram for barbershop system and run simulation.
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Represent the system in GPSS using facility and run simulation for given parts.
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Draw GPSS block diagram to simulate the inspection system for 100 parts.
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Draw GPSS block diagram to simulate supply store problem for 100 requisitions.
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Write short notes on:
a. Simulation tools
Miscellaneous / Cross-Chapter Questions
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Difference between static physical and dynamic physical models.
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Explain traffic intensity and server utilization.
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Explain arrival pattern and service process.
