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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