System Simulation

System simulation is the technique of designing a model of a real-world system and conducting experiments on that model to understand the behavior of the system under various conditions over time.

In simple words, simulation imitates the working of a real system without disturbing the actual system.

Real-Life Examples of System Simulation

Traffic signal simulation to reduce congestion
Flight simulator for pilot training
Bank queue simulation to reduce customer waiting time
Weather forecasting models
Manufacturing plant layout simulation

Need of Simulation

Simulation is required when real-life experimentation is difficult, costly, or risky.

Reasons / Need of Simulation

Real system experimentation may be expensive
Some systems are risky to test (nuclear plants, aircraft)
Real systems may not exist yet
Analytical solutions are not possible
Helps in decision making
Saves time and cost

Need of Simulation

Reason	Explanation	Example
Cost reduction	Avoids real-world testing	Traffic planning
Risk avoidance	Prevents accidents	Nuclear systems
Time saving	Faster experimentation	Queue systems
Flexibility	Easy parameter changes	Inventory systems

Basic Nature of Simulation

The basic nature of simulation describes how simulation systems behave and operate.

Characteristics

Simulation is model-based
It is experimental in nature
Involves time progression
Can handle complex systems
Produces approximate results

Nature Explained with Example

Bank Queue System

Model: Customers, tellers, service time
Time: Arrival and departure over time
Output: Waiting time, queue length

Diagram (Basic Nature)

Real System → Model → Simulation Experiments → Analysis → Decision

Types/Techniques of Simulation

Simulation techniques are methods used to perform simulation studies.

Monte Carlo Simulation

Monte Carlo simulation uses random numbers and probability distributions to simulate uncertain systems.

Features

Random sampling
Probabilistic approach

Example

Stock market prediction
Risk analysis in finance

Discrete Event Simulation

A simulation where changes in the system state occur at discrete points in time.

Example

Bank queue system
Railway reservation system

Continuous Simulation

System variables change continuously over time.

Example

Temperature control system
Water level in dam

Hybrid Simulation

Combination of discrete and continuous simulation.

Example

Manufacturing systems
Traffic systems

Comparison of Simulation Techniques

Technique	Nature	Example
Monte Carlo	Probabilistic	Risk analysis
Discrete Event	Event-based	ATM system
Continuous	Time-based	Climate models
Hybrid	Mixed	Smart city models

Simulation vs Analytical Methods

Analytical Method

Analytical methods use mathematical equations to solve system problems exactly.

Simulation Method

Simulation uses computer-based experimentation to obtain approximate solutions.

Comparison: Simulation vs Analytical Methods

Basis	Simulation Method	Analytical Method
Approach	Experimental	Mathematical
Complexity handling	High	Limited
Accuracy	Approximate	Exact (if solvable)
Time factor	Included	Often ignored
Cost	High initially	Low
Flexibility	Very flexible	Rigid
Realism	High	Low

Advantages of Simulation

Handles complex systems
Safe experimentation
Supports decision making
Visual understanding

Limitations of Simulation

Expensive to develop
Requires skilled personnel
Results are approximate
Time consuming

Short Notes

Simulation imitates real systems
Monte Carlo uses randomness
Discrete simulation is event-driven
Analytical methods need equations

Types of System Simulation

System simulation can be classified based on time, system behavior, and structure.

Broad Classification

Real-Time Simulation
Hybrid Simulation
Monte Carlo Simulation
Queuing System Simulation
Inventory System Simulation
Dynamic Economic Models (Distributed Lag & Cobweb Models)

Real-Time Simulation

Real-time simulation is a simulation in which the system model runs at the same rate as actual time.

Characteristics

Time synchronization with real world
Immediate response required
High accuracy

Real-Life Examples

Flight simulators
Driving simulators
Air traffic control systems
Military training simulators

Diagram

Real World Time = Simulation Time

Advantages

Realistic training
Risk-free environment

Limitations

High cost
Requires powerful hardware

Hybrid Simulation

Hybrid simulation combines continuous simulation and discrete-event simulation.

Characteristics

Handles complex systems
Includes continuous variables and discrete events

Real-Life Examples

Manufacturing systems
Traffic management systems
Power plant operations

Diagram

Continuous Process + Discrete Events → Hybrid Simulation

Simulation of Pursuit Problem

The pursuit problem simulates a situation where one object (pursuer) attempts to catch another object (target).

Key Variables

Speed of pursuer
Speed of target
Direction and distance

Real-Life Examples

Missile tracking an aircraft
Police chasing a vehicle
Predator-prey models

Nature of Model

Dynamic
Continuous-time simulation

Diagram

Pursuer →→→ Target (Moving)

Single-Server Queuing System Simulation

A single-server queuing system has one service facility serving customers one at a time.

Components

Arrival process
Queue
Server
Departure process

Real-Life Examples

Bank counter
Ticket booking window
ATM machine

Diagram

Arrivals → Queue → Server → Departure

Performance Measures

Average waiting time
Queue length
Server utilization\

Simulation of an Inventory Problem

Inventory simulation studies stock levels over time to minimize cost and avoid shortages.

Key Variables

Demand rate
Reorder level
Lead time
Holding cost

Real-Life Examples

Retail store inventory
Warehouse management

Objective

Minimize total inventory cost
Avoid stock-outs

Diagram

Demand ↓ Inventory Level ↑↓ Reorder Point

Monte Carlo Simulation

Monte Carlo simulation uses random numbers and probability distributions to model uncertain systems.

Characteristics

Probabilistic approach
Random sampling

Steps

Define problem
Identify probability distributions
Generate random numbers
Perform simulation
Analyze results

Real-Life Examples

Financial risk analysis
Weather prediction
Project cost estimation

Distributed Lag Model

A distributed lag model explains how the effect of an independent variable is distributed over time.

Explanation: Current output depends on current and past inputs.

Example: Advertising expenditure affects sales not immediately, but over several months.

Simple Representation

Y(t) = a + b1X(t) + b2X(t-1) + b3X(t-2)

Application Areas

Economics
Marketing analysis

Cobweb Model

The Cobweb model explains price and quantity fluctuations due to time lag between production and supply.

Assumptions

Producers decide output based on previous prices
Supply responds with delay

Real-Life Examples

Agricultural products
Seasonal goods

Types of Cobweb Models

Convergent cobweb
Divergent cobweb
Continuous cobweb

Diagram

Price ↑↓ ↔ Quantity ↑↓ (Lag Effect)

Comparison Table

Model	Nature	Application
Real-Time	Time synchronized	Training systems
Hybrid	Mixed	Manufacturing
Monte Carlo	Probabilistic	Risk analysis
Queue Simulation	Discrete	Banking systems
Inventory	Dynamic	Retail
Distributed Lag	Time-lagged	Economics
Cobweb	Dynamic	Agriculture

MCA Exam Tips

Start with clear definitions
Draw simple block diagrams
Use tables for comparison
Give one real-life example per model

System Simulation