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,
admissions)
– Computer network
– Business process
– Chemical plant
– Fast-food restaurant
– Supermarket
– Theme park
– Emergency-response system
3
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
6
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
Spread sheet simulation
[0,T]
11
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.
12
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
13
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
14
Simulation Steps
15
Applications: System Analysis
16
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.
17
Application Area – Auto Tube Manufacturing
1.
1
8
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
1
9
www.flexsim.com
Application Area - Mining
2
0
Application Area – Container Ports – Flexsim CT
21
www.flexsim.com
Application Area – Security Infrastructure –
Border Check point
2
2
www.flexsim.com
Application Area – Aquarium Fish Export
2
3
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.
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
Luz-Church Road, Royapettah High Road,
RamaKrishna-Mutt Road and Kutchery Road in Mylapore
S.Prasanna Venkatesan,
Lect/Prod, NITT
39
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
Computer communication
network: protocol design 42
43
Most unnatural deaths caused by road accidents, suicides: data July 3 2014
44
45
pdf
46
Applications
47
Most unnatural deaths caused by road accidents,
suicides: data July 3 2014
48
49
SIMULATION PACKAGES
50
SIMULATION PACKAGES
51
SIMULATION PACKAGES
52
SELECTION OF SIMULATION PACKAGES
53
S.Prasanna Venkatesan,
Lect/Prod, NITT
54
Geometric simulation systems simulate the geometry of an element or an
entire manufacturing system, usually in three dimensions
S.Prasanna Venkatesan,
Lect/Prod, NITT
55
Journals
S.Prasanna Venkatesan,
Lect/Prod, NITT
56
57
58
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
59
Types of Simulation Models
60
Types of Simulation Models
61
Types of Simulation Models
62
63
A hybrid optimization and simulation approach is emphasized for strategic decisions
under uncertainty.
Fu, Glover and April (2005)
S.Prasanna Venkatesan,
Lect/Prod, NITT
64
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
65
system performance.
Components of DES simulation
Currently in queue
S.Prasanna Venkatesan,
Lect/Prod, NITT
66
Time advance mechanism
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)
S.Prasanna Venkatesan,
Lect/Prod, NITT
67
Fixed increment time advance
S.Prasanna Venkatesan,
Lect/Prod, NITT
68
Fixed increment time advance
69
Next event time advance
Most Imminent first
S.Prasanna Venkatesan,
Lect/Prod, NITT
70
Next event time advance
S.Prasanna Venkatesan,
Lect/Prod, NITT
71
Components of DES simulation
Currently in queue
S.Prasanna Venkatesan,
Lect/Prod, NITT
72
Next event time advance
•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,
73
Lect/Prod, NITT
S.Prasanna Venkatesan,
Lect/Prod, NITT
74
DES Time Advance Program
•
•
•
•
•
•
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.
S.Prasanna Venkatesan,
Lect/Prod, NITT
75
DES Time Advance Program
S.Prasanna Venkatesan,
Lect/Prod, NITT
76
DES Time Advance Program
S.Prasanna Venkatesan,
Lect/Prod, NITT
77
DES Time Advance Program
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
S.Prasanna Venkatesan,
Lect/Prod, NITT
78
DES Time Advance Program
S.Prasanna Venkatesan,
Lect/Prod, NITT
79
DES Time Advance Program
S.Prasanna Venkatesan,
Lect/Prod, NITT
80
Manual simulation DES single server queue
S.Prasanna Venkatesan,
Lect/Prod, NITT
81
Manual simulation DES single server queue
Currently in queue
S.Prasanna Venkatesan,
Lect/Prod, NITT
82
S.Prasanna Venkatesan,
Lect/Prod, NITT
83
S.Prasanna Venkatesan,
Lect/Prod, NITT
84
S.Prasanna Venkatesan,
Lect/Prod, NITT
85
S.Prasanna Venkatesan,
Lect/Prod, NITT
86
S.Prasanna Venkatesan,
Lect/Prod, NITT
87
S.Prasanna Venkatesan,
Lect/Prod, NITT
88
Measures of performance
S.Prasanna Venkatesan,
Lect/Prod, NITT
89
S.Prasanna Venkatesan,
Lect/Prod, NITT
90
S.Prasanna Venkatesan,
Lect/Prod, NITT
91
S.Prasanna Venkatesan,
Lect/Prod, NITT
92
Measures of performance
Product of previous value of Q (t) and the width of time interval between from last event to now