Robotics Basics Chapter 1, Lecture notes of Robotics and Autonomous Systems

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Chapter 1

Introduction to

Simulation

Banks, Carson, Nelson & Nicol Discrete-Event System Simulation

Outline „^ When Simulation Is the Appropriate Tool „^ When Simulation Is Not Appropriate „^ Advantages and Disadvantages of Simulation „^ Areas of Application „^ Systems and System Environment „^ Components of a System „^ Discrete and Continuous Systems „^ Model of a System „^ Types of Models „^ Discrete-Event System Simulation „^ Steps in a Simulation Study

Goal of modeling and simulation „^ A model can be used to investigate a wide verity of “whatif” questions about real-world system.^

Potential changes to the system can be simulated and predicatetheir impact on the system. Find adequate parameters before implementation „^ So simulation can be used as^

Analysis tool for predicating the effect of changes Design tool to predicate the performance of new system „^ It is better to do simulation before Implementation.

How a model can be developed? „^ Mathematical Methods^

Probability theory, algebraic method ,… Their results are accurate They have a few Number of parameters It is impossible for complex systems

„^ Numerical computer-based simulation^

It is simple It is useful for complex system

When Simulation Is Not Appropriate „^ When the problem can be solved by commonsense. „^ When the problem can be solved analytically. „^ If it is easier to perform direct experiments. „^ If cost exceed savings. „^ If resource or time are not available. „^ If system behavior is too complex.^

Like human behavior

Advantages and disadvantages of simulation „^ In contrast to optimization models, simulationmodels are “run” rather than solved.^

Given as a set of inputs and model characteristics themodel is run and the simulated behavior is observed

Disadvantages of simulation „^ Model building requires special training.^

Vendors of simulation software have been activelydeveloping packages that contain models that onlyneed input (templates).

„^ Simulation results can be difficult to interpret. „^ Simulation modeling and analysis can be timeconsuming and expensive.^

Many simulation software have output-analysis.

Areas of application „^ Manufacturing Applications „^ Semiconductor Manufacturing „^ Construction Engineering and project management „^ Military application „^ Logistics, Supply chain and distribution application „^ Transportation modes and Traffic „^ Business Process Simulation „^ Health Care „^ Automated Material Handling System (AMHS)^

Test beds for functional testing of control-system software „^ Risk analysis^

Insurance, portfolio,... „^ Computer Simulation^

CPU, Memory,… „^ Network simulation^

Internet backbone, LAN (Switch/Router), Wireless, PSTN (call center),...

Components of system „^ Entity^

An object of interest in the system : Machines in factory „^ Attribute^

The property of an entity : speed, capacity „^ Activity^

A time period of specified length :welding, stamping „^ State^

A collection of variables that describe the system in any time : status of machine(busy, idle, down,…) „^ Event^

A instantaneous occurrence that might change the state of the system:breakdown „^ Endogenous^

Activities and events occurring with the system „^ Exogenous^

Activities and events occurring with the environment

Discrete and Continues Systems „^ A discrete system is one in which the state variableschange only at a discrete set of points in time : Bankexample

Model of a System „^ To study the system^

it is sometimes possible to experiments with system^ „^ This is not always possible (bank, factory,…)^ „^ A new system may not yet exist

„^ Model: construct a conceptual framework thatdescribes a system^

It is necessary to consider those accepts of systemsthat affect the problem under investigation(unnecessary details must remove)

Types of Models

Discrete-Event Simulation Model „^ Stochastic: some state variables are random „^ Dynamic

: time evolution is important

„^ Discrete-Event

: significant changes occur at

discrete time instances

Model Taxonomy^ Model Taxonomy