# Des

Published on June 2016 | Categories: Types, School Work | Downloads: 77 | Comments: 0 | Views: 560
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Automation

## Content

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Representing system
• System:
– a collection of mutually interacting objects
designed to accomplish a goal (machines repair
system)

• Entities:
– denotes an element/object within boundary of
system (machines, operators, repairman)
• Entity – work being performed on object
• Resource – performing the work

System
– Manufacturing facility/ system
– Bank operation
– Airport operations (passengers, security, planes, crews,
baggage)
– Transportation/logistics/distribution operation
– Hospital facilities (emergency room, operating room,
– Computer network
– Chemical plant
– Fast-food restaurant
– Supermarket
– Theme park
– Emergency-response system

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Representing system
• Attribute:
– Characteristic or property or an entity (machine
ID, Type of breakdown, time that machine went
down)

• Activity:
– transforms the state of an object usually over
some time (repairman service time, machine run
time)

Representing system
• State of the system:
– Numeric values that contain all the information
necessary to describe the system at any time.

• Events:
– Change the state of the system(end of service of
machine,machine breaks down)

Endogenous

Activities and events occurring with the system

Exogenous

Activities and events occurring with the environment

Types of Simulation Models
System model

Stochastic

Deterministic

Static

Dynamic

Static

Dynamic

Monte Carlo simulatio
n
Continuous

Discrete

Continuous

Discrete

Discrete-event
simulation
Simulates the behavior of entities when an event occurs at a distinct point in time

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Types of Simulation Models
• A deterministic simulation model is one
that contains no random variables;
• A stochastic simulation model contains one
or more random variables

Types of Simulation Models
• A
static
simulation
model
is
a
representation of a system at a particular
point in time. [Monte Carlo simulation]
• A dynamic simulation is a representation of
a system as it evolves over time.

Types of Simulation Models
Discrete event:
state of system changes only at discrete points in
time(events)

Types of Simulation Models
Continuous event:
State of system changes continuously over time

Simulation methods

[0,T]

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System dynamics is an approach to understanding the behaviour of
complex systems over time. It deals with internal feedback loops and time
delays that affect the behavior of the entire system.

Simulation of such systems is easily accomplished by partitioning
simulated time into discrete intervals of length dt and stepping
the system through time one dt at a time.

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Discrete Event Simulation
Modeling of a system as it evolves overtime by a representation
where the state variables change instantaneously at separated
points in time

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Simulation Steps
Model
conceptualization

Problem
formulation

Setting of
objectives
and overall
project plan

No

Experimental
Design
Yes

Model
translation

Verified?

Yes

Validated?

Production runs
and analysis

No
Yes

Data
collection

No

Yes
More runs?
No

Implementation

Documentation
and reporting

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Simulation Steps

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Applications: System Analysis

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SIMULATION TYPICAL APPLICATIONS

Facility Layout.
Sequencing & Optimization In Assembly Line.
Capital Expenditure Assessment.
Capacity Requirement Planning.
Production Scheduling.
Production Process Improvement.
Supply Chain Logistics.
Service Level Reliability.
Labour Utilization.
Intermediate Storage.
Batch Production Sequencing.
Annual Delivery Program.

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Application Area – Auto Tube Manufacturing

1.

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www.flexsim.com

Improve equipment
utilization
2. Reduce waiting time
and queue sizes
3. Allocate
resources
efficiently
4. Eliminate stock-out
(shortage) problems
5. Minimize
negative
effects
of
breakdowns
6. Minimize
negative
effects of rejects and
waste
7. Study cost reduction
plans
8. Establish
optimum
batch sizes and part
sequencing
9. Resolve
material
handling issues
10. Study effect of setup
times
and
tool
changeovers
11. Optimize
prioritization
and
dispatching logic for
goods and services
12. Demonstrate
new
tool
design
and
capabilities

Application Area – Packaging line design

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www.flexsim.com

Application Area - Mining

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0

Application Area – Container Ports – Flexsim CT

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www.flexsim.com

Application Area – Security Infrastructure –
Border Check point

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2

www.flexsim.com

Application Area – Aquarium Fish Export

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www.flexsim.com

Application Area – Emulation

PLSee is a plug-in module that
enables communication between a
running Flexsim
simulation and almost any PLC

Emulation should allow you to go from testing to
deployment with no code changes.
Emulation should work like the real world.

www.flexsim.com

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Application Area – Healthcare – Flexsim HC

Medical facilities
are

among

the

most complex in
the

world.

Numerous factors
contribute

to

overall efficiency
and

work-flow,

including:
patient flow
staff utilization
resource management
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www.flexsim.com

Wagner and Neshat
(2010)

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Simulation survey
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Traffic-Signal Time Settings by Using
Simulation

S.Prasanna Venkatesan,
Lect/Prod, NITT

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Problem Statement
The modelling of traffic systems is really difficult
complexity of road networks and random operation of
vehicles.
Objective of minimizing the total delay caused to the vehicles at
the intersection.
The signalized intersection connecting

S.Prasanna Venkatesan,
Lect/Prod, NITT

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Applications: On-Line Decision Aids

live
data
feeds

interactive
simulation
environment

situation
database

analysts and
decision makers

forecasting tool
(fast simulation)

Simulation tool is used for fast analysis of alternate courses of action in
time critical situations
– Initialize simulation from situation database
– Faster-than-real-time execution to evaluate effect of decisions

Applications: air traffic control
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Applications: On-Line Decision Aids

Air traffic control
software failure

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A Few Example Applications

Wargaming: test
strategies; training

Parallel computer systems:
developing scalable software

Transportation systems:
improved operations;
urban planning

Computer communication
network: protocol design 42

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Most unnatural deaths caused by road accidents, suicides: data July 3 2014

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pdf

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Applications

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Most unnatural deaths caused by road accidents,
suicides: data July 3 2014

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SIMULATION PACKAGES

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SIMULATION PACKAGES

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SIMULATION PACKAGES

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SELECTION OF SIMULATION PACKAGES

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S.Prasanna Venkatesan,
Lect/Prod, NITT

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Geometric simulation systems simulate the geometry of an element or an
entire manufacturing system, usually in three dimensions

S.Prasanna Venkatesan,
Lect/Prod, NITT

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Journals

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Discrete Event Simulation

to

An actual or envisioned system

A useful simulation model of that system

Modeling of a system as it evolves overtime by a
representation where the state variables change
instantaneously at separated points in time
S.Prasanna Venkatesan,
Lect/Prod, NITT

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Types of Simulation Models

60

Types of Simulation Models

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Types of Simulation Models

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A hybrid optimization and simulation approach is emphasized for strategic decisions
under uncertainty.

Fu, Glover and April (2005)
S.Prasanna Venkatesan,
Lect/Prod, NITT

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Components of DES simulation
Simulation clock: A variable giving the current value of simulated time. Unit of
time is assumed to be same as unit of input parameters
Activity: A duration of time of specified length which is known when it begins
eg. Arrival, Service time
List/set: A collection of associated entities ordered in some logical fashion
e.g. In an outpatient clinic a set might include the patience waiting for service
ordered by severity of disorder or first come first serve
Event notice: A record of an event to occur at the current or future time along
with associated data to execute the event.
Event List/Future Event List: A list of event notices for future events ordered by
time of occurrence
Delay: A duration of time of unspecified length which is not known until it ends
e.g. waiting time in queue
Statistical counters: Variables used for storing statistical information about the
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system performance.

Components of DES simulation

Currently in queue

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Lect/Prod, NITT

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To advance the time from current event to the next scheduled
event
Two approaches:
Fixed increment time advance (Seldom used)
Next event time advance (Most common)

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Lect/Prod, NITT

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Most Imminent first

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Components of DES simulation

Currently in queue

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Lect/Prod, NITT

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•Assume that the probability distributions of the inter arrival times A1, A2, …
and the service times S1, S2, … are known
•At time e0 = 0 the status of the server is idle, and the time t1 of the first
arrival is determined by generating A1
•The simulation clock is then advanced from e0 to the time of the next (first)
event, e1 = t1. status of the server is changed from idle to busy. Delay is zero.
•Generate S1, A2. If t2 < c1, the simulation clock is advanced from e1 to the
next event e2 = t2 else to c1
S.Prasanna Venkatesan,
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Lect/Prod, NITT

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Initialization routine – a subprogram to Initialise the simulation model at time
zero
Timing routine – a subprogram that determines the next event from the event
list and then advances the simulation clock to the time when the event is to
occur.
Event routine – a subprogram that updates the system state when a particular
type of event occurs
Library routines – a set of subprograms used to generate random observations
from probability distributions that were determined as part of the simulation
model
Report generator – a subprogram that computes estimates of the desired
measures of performance and produces a report when the simulation ends
Main program – a subprogram that invokes the timing routine to determine the
next event and then transfers control to the corresponding event routine to
update the system state. The main program may also check the termination
and invoke the report generator when the simulation is over.
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Lect/Prod, NITT

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Two techniques to generate future events
Bootstrapping occurrence of an event generates next
occurrence of the same type of event
Next Logical event e.g. Service completion generates
next event
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Manual simulation DES single server queue

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Manual simulation DES single server queue

Currently in queue

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Measures of performance

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Measures of performance

Product of previous value of Q (t) and the width of time interval between from last event to now

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Measures of performance

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