Introduction to System, Simulation and Model

Introduction To
System, Simulation
and Model
Presentation Title

Group Member
Md. Mahbubur Rahman| SID: 182-15-11742
Dept. of Computer Science and Engineering
Email: hasan15-11743@diu.edu.bd
Daffodil International University, Dhaka, Bangladesh.
Aysha Siddika | SID: 182-15-11639
Md. Hasan Imam Bijoy | SID: 182-15-11743

Presentation Outline
1 What is System?
2 Component of System
4 Concept of Simulation
1 Concept of Model
5 Types of Simulation
6 Workflow of Simulation
7 Advantages
9 Application of Simulation
8 Disadvantages

System
 A system is a simplified representation of reality.
 System defined as a group of objects that are joined together in some regular interaction or
interdependence toward the accomplishment of some purpose
 Whereas in the real world, a "system" may seem at times an endless series of connected elements.
 We refer here to a system as
 A series of selected, chosen elements (this is a first simplification, and thus an implicit
assumption)
 Specified boundaries (a second simplification and implicit assumption)
 Pre-determined time characteristics (with a third simplification and implicit assumption).

Component of System
 Entity : an object of interest in the system.
 Attribute : a property of an entity.
 Activity : a time period of specified length.
 State : the collection of variables necessary to describe the system at any time,
relative to the objectives of the study.
 Event : an instantaneous occurrence that may change the state of the system.
 Endogenous : to describe activities and events occurring within a system.
 Exogenous : to describe activities and events in an environment that affect the
system.

Components of Banking System
State Variable
- Number of Busy tellers
- Number of Customer Waiting
Attributes
- Checking Account Balance
Entity
- Customer or Client
Activates
- Making Deposits
Event: Arrival and Departure
Banking System

Type of System
Continuous
Continuous Simulation refers to a computer
model of a physical system that continuously
tracks system response according to a set of
equations typically involving differential
equations.
Discrete
Discrete event simulation (DES) is a method
used to model real world systems that can be
decomposed into a set of logically separate
processes that autonomously progress through
time.
Time
Heat
of
Water
Time
Banking
Queue

Model in Simulation
A representation of a system for the purpose
of studying the system
Sufficiently detailed to permit valid
conclusions to be drawn about the real system
A simplification of the system
Model in Simulation

Types of Model
Model
Static or Dynamic
• Static simulation model (called Monte
Carlo simulation) represents a system
at a particular point in time.
• Dynamic simulation model represents
systems as they change over time
Determinic or Stochastic
• Deterministic simulation models contain no
random variables and have a known set of
inputs which will result in a unique set of
outputs
• Stochastic simulation model has one or more
random variables as inputs. Random inputs
lead to random outputs.
The model of interest in this class is discrete, dynamic, and stochastic.

Simulation
A simulation is the imitation of the operation
of a real-world process or system over time.
whereas the simulation represents the
evolution of the model over time.
Simulations require the use of models; the model
represents the key characteristics or behaviors of
the selected system or process
Simulation

Types of Simulation
Simulation System Dynamics Simulation
This is a very abstract form of simulation
modeling. Unlike agent-based modeling
and discrete event modeling, system
dynamics does not include specific details
about the system.
Agent-Based Modeling & Simulation
An agent-based simulation is a model that
examines the impact of an ‘agent’ on the
‘system’ or ‘environment.’ In simple terms, just
think of the impact a new laser-cutter or some
other factory equipment has on your overall
manufacturing line.
Monte Carlo
Monte Carlo simulation is a method of risk
analysis. Businesses use it prior to
implementing a major project or change in
a process, such as a manufacturing
assembly line.
Discrete Event Simulation
A discrete event simulation model enables
our to observe the specific events that result
in our business processes. We would use a
discrete event simulation model to examine
that technical support process

Steps in Simulation
Policy maker/Analyst understand and
agree with the formulation.

Steps in Simulation
Setting of objectives and overall project
plan

Steps in Simulation
The art of modeling is enhanced by
an ability to abstract the essential
features of a problem, to select and
modify basic assumptions that
characterize the system, and then to
enrich and elaborate the model until
a useful approximation results.

Steps in Simulation
As the complexity of the model
changes, the required data elements
may also change

Steps in Simulation
GPSS/HTM or special-purpose
simulation software

Steps in Simulation
Is the computer program performing properly?
Debugging for correct input parameters and
logical structure

Steps in Simulation
The determination that a model is an accurate
representation of the real system.
Validation is achieved through the calibration of
the model

Steps in Simulation
The decision on the length of the initialization
period, the length of simulation runs, and the
number of replications to be made of each run.

Steps in Simulation
To estimate measures of
performances

Steps in Simulation
Decision to more Runs

Steps in Simulation
Program documentation : for the
relationships between input
parameters and output measures of
performance, and for a modification
Progress documentation : the history
of a simulation, a chronology of work
done and decision made.

Steps in Simulation
Implementation of Programs

Advantages
 New polices, operating procedures, decision rules, information flows, organizational procedures, and so on can be
explored without disrupting ongoing operations of the real system.
 New hardware designs, physical layouts, transportation systems, and so on, can be tested without committing resources
for their acquisition.
 Hypotheses about how or why certain phenomena occur can be tested for feasibility.
 Insight can be obtained about the interaction of variables.
 Insight can be obtained about the importance of variables to the performance of the system.
 Bottleneck analysis can be performed indicating where work-in-process, information, materials, and so on are being
excessively delayed.
 A simulation study can help in understanding how the system operates rather than how individuals think the system
operates.
 “What-if” questions can be answered. This is particularly useful in the design of new system.

Disadvantages
 Model building requires special training. It is an art that is learned over time and through experience.
Furthermore, if two models are constructed by two competent individuals, they may have similarities, but
it is highly unlikely that they will be the same.
 Simulation results may be difficult to interpret. Since most simulation outputs are essentially random
variables (they are usually based on random inputs), it may be hard to determine whether an observation
is a result of system interrelationships or randomness.
 Simulation modeling and analysis can be time consuming and expensive. Skimping on resources for
modeling and analysis may result in a simulation model or analysis that is not sufficient for the task.
 Simulation is used in some cases when an analytical solution is possible, or even preferable. This might
be particularly true in the simulation of some waiting lines where closed-form queueing models are
available.

Area of Application
 Manufacturing Applications
 Analysis of electronics assembly operations
 Design and evaluation of a selective assembly station for high-precision scroll compressor
shells
 Comparison of dispatching rules for semiconductor manufacturing using large-facility models
 Evaluation of cluster tool throughput for thin-film head production
 Determining optimal lot size for a semiconductor back-end factory
 Optimization of cycle time and utilization in semiconductor test manufacturing
 Analysis of storage and retrieval strategies in a warehouse
 Investigation of dynamics in a service-oriented supply chain
 Model for an Army chemical munitions disposal facility
 Semiconductor Manufacturing
 Comparison of dispatching rules using large-facility models
 The corrupting influence of variability
 A new lot-release rule for wafer fabs

Area of Application
 Logistics, Transportation, and Distribution Applications
 Evaluating the potential benefits of a rail-traffic planning algorithm
 Evaluating strategies to improve railroad performance
 Parametric modeling in rail-capacity planning
 Analysis of passenger flows in an airport terminal
 Proactive flight-schedule evaluation
 Logistics issues in autonomous food production systems for extended-duration space exploration
 Sizing industrial rail-car fleets
 Product distribution in the newspaper industry
 Design of a toll plaza
 Choosing between rental-car locations
 Quick-response replenishment

Area of Application
 Construction Engineering
 Construction of a dam embankment
 Trenchless renewal of underground urban infrastructures
 Activity scheduling in a dynamic, multiproject setting
 Investigation of the structural steel erection process
 Special-purpose template for utility tunnel construction
 Military Application
 Modeling leadership effects and recruit type in an Army recruiting station
 Design and test of an intelligent controller for autonomous underwater vehicles
 Modeling military requirements for nonwarfighting operations
 Multitrajectory performance for varying scenario sizes
 Using adaptive agent in U.S Air Force pilot retention

Area of Application
 Business Process Simulation
 Impact of connection bank redesign on airport gate assignment
 Product development program planning
 Reconciliation of business and systems modeling
 Personnel forecasting and strategic workforce planning
 Human Systems
 Modeling human performance in complex systems
 Studying the human element in air traffic control

Introduction to System, Simulation and Model

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