CSE10

Simulation

The imitation of a real world process or system overtime, usually via a model.

Deterministic

No randomness; same input = same output

Stochastic

Includes randomness; different runs = different results

System

A collection of interacting components forming a whole

Model

A simplified representation of a system

Simulation

Using the model to study system behavior

Continuous

System changes smoothly over time

Discrete

System changes at events

Stochastic

Random behavior included

Static

No time dimension

Dynamic

Includes time evolution

Analytical

Solved mathematically

Simulation based

Solved using computer models

Designing and analyzing manufacturing system

Simulation helps test : Machine layouts, production schedules, bottlenecks, downtime and delays

Determining hardware requirements or protocols for communication networks

Simulations helps determine : required bandwidth, router capacity, best communication protocols

Determining hardware and software requirements for a computer system explanation

Simulation is used to estimate : CPU speed, memory requirements software response time

Designing and operating transportation system

Simulation systems involve : vehicles, passengers, traffic signals, time constraints

Evaluating service organizations

Simulation system involve : customer waiting time, number of servers, service speed

Re-engineering of business processes explanation

Simulation system involves : old process vs. new process, delays and redundancies

Analyzing supply chains

Simulation system involves : Suppliers, manufacturers, warehouses, retailers

Determining ordering policies for an inventory system

Simulation system involves : shortages, overstocking

Analyzing mining operations

Simulation system involves: equipment, workers, transportation, safety concerns

Discrete- Event Simulation

A system in which state changes occur only at specific points in time, called events. Between events, the system remains unchanged.

Continuous Simulation

System where state variable changes continuously overtime, usually described by mathematical equations.

Stochastic simulation

Includes randomness or probability in the model. At least one system variable is random, making outcomes uncertain

Model translation

Convert the conceptual model into a computer program

Verification

Check whether the model is correctly implemented. Ensure the logic and code work as intended

Validation

Compare simulation results with the real system. Check if the model accurately represents reality

Experimental design

Number of simulations run, simulation length parameter variation

Simulation execution

Run the simulation based on the experimental design. Collect output data

Output analysis and interpretation

Analyze results using statistics or graphs. Interpret findings for decision making

Documentation and reporting

Document assumptions, model details , and results

Implementation

Apply recommendations to the real system