Generating random numbers which are observations from a U(0,1) probability distribution

Generating random variates from a specified probability distribution

Advancing simulated time

Determining the next event from the event list and passing control to the appropriate block of

code

Adding records to, or deleting records from, a list

Collecting output statistics and reporting the results

Detecting error conditions

Simulation software

Simulation packages

Programming languages

Common Modeling Elements

entities

attributes

resources

queues

Desirable software features

General capabilities (including modeling flexibility and ease of use)

Hardware and software considerations

Animation

Statistical features

Customer support and documentation

Output reports and plots

• Modeling flexibility

• Ease of use

• Hierarchy

• Debugging aids

• Fast model execution speed

• User-friendly model “front ends”

• Run-time version

• Import/export data from/to other applications

• Cost module

• Combined discrete-continuous simulation

• External routines

• The state of a simulation can be saved at the end of a run

• Cost of simulation software

General Capabilities

• Ability to define and change attributes for entities and also global variables, and to use both

in decision logic

• Ability to use mathematical expressions and mathematical functions

• Ability to create new modeling constructs and to modify existing ones, and to store them in

libraries for use in current and future models

Flexible Capabilities

• With a good random-number generator, the generator should have at least 100 different streams that can be assign to different sources of randomness in a simulation model

• Produce the same results on different executions if the default seeds are used for the various streams, and the user should be able to set the seed for each stream

• Standard theoretical distribution should be found and used into a model

• Triangular distribution is often used as a model for a source of randomness when no system data are available

• Getting confidence interval for a mean by replication running

• To specify a warm up period can make statistical counter calculating more accurate data for “steady-state”

• With optimization capability

Statistical Capabilities

Uses of simulation have evolved with hardware, software

The early years (1950s-1960s)

Very expensive, specialized tool to use

Required big computers, special training

Mostly in FORTRAN (or even Assembler)

Processing cost as high as $1000/hour for a sub-286 level machine

The formative years (1970s-early 1980s)

Computers got faster, cheaper

Value of simulation more widely recognized

Simulation software improved, but they were still languages to be learned,

typed, batch processed

Often used to clean up “disasters” in auto, aerospace industries

• Car plant; heavy demand for certain model

• Line underperforming

• Simulated, problem identified

• But demand had dried up — simulation was too late

The recent past (late 1980s-1990s)

Microcomputer power

Software expanded into GUIs, animation

Wider acceptance across more areas

• Traditional manufacturing applications

• Services

• Health care

• “Business processes”

Still mostly in large firms

Often a simulation is part of the “specs”

The present

Proliferating into smaller firms

Becoming a standard tool

Being used earlier in design phase

Real-time control

The future

Exploiting interoperability (互通性) of operating systems

Specialized “templates” for industries, firms

Automated statistical design, analysis

Networked sharing of data in real time

Integration with other applications

Distributed model building, execution

Model Building Features

Modelling worldview

Input-data analysis capability

Graphical model-building

Simulation programming

Syntax

Input flexibility

Modeling conciseness (简洁性)

Randomness

Specialized components and templates

User-built custom objects

Continuous flow

Interface with general-programming language

Runtime Environment

Execution speed

Model size; number of variables and attributes

Interactive debugger

Model status and statistics

Runtime license

Animation and layout features

Type of animation

Import drawing and object files

Dimension

Movement

Quality of motion

Libraries of common objects

Navigation

Views

Display step

Selectable objects

Hardware requirements

Output Features

Scenario manager

Run manager

Warm-up capability

Independent replications

Optimization

Standardized reports

Customized reports

Statistical analysis

Business graphics

Costing module

File export

Database maintenance

Vendor Support and Product Documentation

Training

Documentation

Help system

Tutorials

Support

Upgrades, maintenance

Track record

Arena www.arenasimulation.com

AutoMod www.automod.com

Extend www.imaginethatinc.com

Flexsim www.flexsim.com

ProModel www.promodel.com

SIMUL8 www.simul8.com

WITNESS www.witness-for-simulation.com

SIMIO www.simio.com

Usage

Arena is a simulation environment consisting of module

templates, built around SIMAN language constructs and

other facilities, and augmented by a visual front end

Widely used in academic research and engineering

for discrete-event simulation and continuous simulation

Edition

Basic Edition

Standard

Professional

Characteristics

Intelligent object-based simulation software

Full 3D designing and running environment

Plenty of application packages

External language integration

Characteristics

AnyLogic is an extremely flexible simulation tool. Ability to extend the built-in modeling language

with virtually any Java code makes any model implementable with AnyLogic

The only simulation tool that supports Discrete Event, Agent Based, and System Dynamics

Simulation within one modeling language and one model development environment

An AnyLogic model is completely separable from the development environment and can be

exported as a standalone Java application

Common features

In recent years, many packages have added optimization as one of the analysis tools

Optimization is used to find a “near-optimal” solution. The user must define an objective or fitness

function, usually a cost of cost-like function that incorporates the trade-off between additional

throughput and additional resources.

Some popular algorithms of optimization are used in simulation packages, such as artificial

intelligence, neural network, genetic algorithm, evolutionary strategies, Tabu search and scatter

search

Products

Arena Output and Process Analyzer

Autostat for AutoMod

OptQuest (used in a number of simulation products)

Simrunner for Promodel