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Dr. Pramod. M1
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A set of integrated resources (machinery, labourer,
equipment, information, and tools) that process raw
material as input and produce final products as
outputs.
Its Purpose:
Meet customer requirements
Add Value
At Minimum cost High Quality and Reliable
Environmentally Friendly
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The responsibilities of management are: Establish priorities
Utilise resources
Monitor Performance
Measure Performance
Improve productivity (yield) and efficiency(input/output)
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Production Machines, tools, fixtures, and other
hardware equipment
Material Handling Systems
Loading and unloading (batch control)
Positioning (manual, automated)
Transporting (conveyors, transporters)
Temporary storage (buffers)
Computer Control Systems (SCADA, Robotics,Scheduling, Safety Monitoring, Quality, )
Human Resources
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Types of operations
Number of workstations and system layout
Product variety
Level of automation
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Processing operations: Working on individual parts
(e.g. metal sheets, rolling, machining, drilling,
treating, painting, etc.)
Assembly operations: combining and putting parts
together e.g. (mounting gearbox, engines dressing,
Trimming shops in car factories, etc.)
Type of parts and products: The specification of the
material and also the method of processing the part
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Key factorin classification scheme
Determines main performance factors such as
capacity, capability, efficiency, productivity,utilisation, cost per unit, and maintainability
Determines the complexity of operations
Arrangement of workstations is called System
Layout
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Type I: Single Station where (n = 1) Type II: Multiple Stations with fixed routings
where (n > 1)
Type III: Multiple Stations with variable routing
where (n >1)
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Characteristics Product Process Group (Cell) Fixed Pos.
Throughput time Low High Low Medium
WIP Low High Low Medium
Skill Levels Choice High Med-High Mixed
Product Flexibility Low High Med-High High
Demand Flexibility Medium High Medium Medium
Machine Utilisation High Med-Low Med-High Medium
Operator Utilisation High High High Medium
Production cost/unit Low High Low High
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First Law (Littles Law): in a steady state system,WIP = Production Rate x Throughput Time
Second Law: Matter is conserved
Raw material enter the system (input) and finished products exitthe system (output). Any remaining or rejected parts need to beaccounted for.Therefore the summation of entry should be equal:-Total Material
Entry (input) = Finished Parts (output) + Removed Material +
Disposed Material + Recycled Material
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Third Law: The larger the system the less reliableit is
Fourth Law: Objects decay
Both hardware and software objects decay over time,they need maintenance and replacement
Fifth Law: Exponential growth in complexityComplexity increases with a larger rate when
components are added to the system.
Sixth Law: Technology advances
Natural evolution to better material, processes andinformation
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Principles of Manufacturing Systems
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Seventh Law: System components appear to behaverandomlyEvents can not be precisely predicted and this needs to be observed in anysystem design, development and analysis
Eighth Law: Limits of human rationalityHuman beings have limitations, this should be accounted for in any systemanalysis
Ninth Law: Combining, Simplifying, and Eliminating
save Time, Money and EffortKISS concept Good models are abstract, straight to the point, accurateand specified objectives.
Over complication is Lethal
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Simulation is a powerful tool for modeling and
analysis of complex systems.
Many real life problems are difficult to study via
analytical methods. But simulation model can be
constructed and run for all types of problems to
generate information on the system performance-
How well the system performs for a set of parameters How to optimize the system
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Used mainly as a decision making tool
Until 1980s , simulation was costly and time
consuming but with the advent of PC and
powerful hardware- fast, low cost, interactive,
visualization and animation oriented simulation
was possible
Arena general purpose visual simulation
environment
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Simplified representation of a system under study
Experiment with the system with a set of goal like
improve system design, cost benefit analysis,
sensitivity analysis
Representation describes system structure while
histories generated describes system behavior.
Model- simplified representation of a complex system
to capture behavioral aspects interested to the analyst
to gain insight into the behavior of the system--
abstraction & simplification16
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Evaluating a systems performance under ordinaryand unusual scenarios
Predicting performance of experimental system
design
Ranking multiple design and their trade offs
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Worldview is a philosophy-----two types : developer
WV and user WV
Developer WV----discrete event simulation
paradigm, model possesses a state at any point in time and the state
remains unchanged unless a simulation event occurs thenthe model undergoes transition.
Model evolution is controlled by a clock and an event list,each event is a code which can change the state variableand schedule other events
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Problem analysis and information collection-
identification of input, performance measures,
relationship among parameters, constraints, flow
diagrams and trees...
Data collection-
estimate model input parameters and assumptions
Model construction- either using computer language or using special
simulation environment like arena
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Model verification-
make sure that the model is correctly constructed as
per specification
Model validation-
fit of the model to empirical data. Good fit means
performance measure predicted by the model match
or agree reasonably with those in real life systems
Designing and conducting simulation runs
Output analysis & Final recommendation
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Discrete event simulation Probability and statistics
Random number generation
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Provides an integrated framework for building
simulation models in a wide variety of
applications. It integrates all the functions needed
for a successful simulation including:
1. Animation
2. analysis of inputs and outputs data
3. model verificationinto one comprehensive environment.
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Consists of modular templates build around SIMAN
language constructs and visual front end SIMAN consists of two classes of objects- blocks and
elements
Blocks are logical constructs that represent operations(SEIZE/ RELEASE)
Elements are objects that represent a facility
(RESOURCES, QUEUES, TALLIES....)
Arena fundamental modeling component is the
modules which is a high level construct consisting of
many SIMAN blocks and elements.
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a unique collection of small example models that
demonstrate a variety of modeling techniques and
situations commonly encountered using Arena.
SMARTs have been specifically designed for use as
a training or reference tool to assist you in your
model-building efforts .
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Entities : In every simulation model entities are
objects under a particular process, and they move
along the system.
Example: manufacturing raw material and products, banks
customers, hospitals patients are the entities.
In a typical Simulation project there can be one or
many different types of entities.
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Attributes : Are the characteristics of each entity
and represent values associated with individual
entities. In a typical system we can define as many attributes as we
need for the entities. Example: length, or weight, or patients the type of the
disease.
Stations : Stations are boundaries where
processing occurs in a system. Example: Production line a process is performed by a station
i.e. drilling, milling, filling, etc. And in offices these boundariescould be departments.
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Transporters : Entities move in the system via
transporters.
Transporters can be used to represent material handling or
transfer of devices.
Some examples for transporters are; AGVs, trucks, forks,
cranks, carts, etc
Conveyors : Conveyors are devices that move
entities form one station to another in one direction.
Such as escalators and horizontal conveyors.
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Variables : Represent values that describe the
characteristics of the system. These values are available to
all entities.
Used for many different kinds of things
Travel time between all station pairs Number of parts in system (Work-in Process)
Simulation clock (built-in Arena variable)
Name, value of which theres only one copy for the whole model
Not tied to entities
Entities can access, change variables
Some built-in by Arena, you can define others
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Consider a single WS with an m/c of infinite buffer.
jobs arrive randomly and wait in buffer if the m/c isbusy. The IAT are expo(30)min while PT are
expo(24)min. (M/M/1 Queue). System simulated for
10000 hrs Data:-
IAT are expo(30)min
PT are expo(24)min
Simulation run time-10000 hrs
Compare with theoretical results, estimate avg job
delay in Q, avg no in Q and m/c utilization
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Result:- (insufficient ) no of observations insufficient for adequate
statistical confidence.
Number busy- no of busy units of a resource
Number schedule- resource capacity
Inst utilization-utilization per resource unit= number busy/number
scheduled
For M/M/1- m/c utilization ==/ Where = job arrival rate = 1/30,
And = job processing rate= 1/24
= 0.8 (0.81 obtained through simulation)
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Consider a manufacturing network of two workstations
in series, consisting of an assembly workstationfollowed by a painting workstation, where jobs arrive
at the assembly station with exponentially distributed
inter-arrival times of mean 5 hours.
the assembly process always has all the raw materials
necessary to carry out the assembly operation the
assembly time is uniformly distributed between 2 and 6
hours
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after the process is completed, a quality control test is
performed, and past data reveal that 15% of the jobs
fail the test and go back to the assembly operation for
rework jobs that pass the test proceed to the painting
operation that takes 3 hours for each unit.
We are interested in simulating the system for 100,000
hours estimating process utilizations, average job
waiting times and average job flow times (the elapsed
time for a job from start to finish)
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Data- IAT=Expo(5)hr PT
assembly=Unif(2,6)hr QC=85% True, 15% false --
---rework PT paint= 3 hrs
Simulation time=100000hrs Attributes=Tnow, total
rework Record= flow time=
time between jobdeparture from paintand arrival time atassembly, reworks / job
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time between arrival of successive batch is expo
(30)min. Upon arrival at the prep area it is separated
into 4 units which are processed individually from here.
The processing at the prep area follows TRIA (3,5,10).
The part is then sent to the sealer.
At the sealer , the case is sealed and tested, the total
time for these operations depends on the part type;
TRIA (1,3,4)for part a and WEIB(2.5,5.3) for Part B.
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91 % of the parts pass inspection and are transferred
directly to the shipping dept.
The remaining parts are transferred to the rework area
where they are disassembled, repaired cleaned
assembled and retested. 80 % of the parts are salvaged
and passed on to the shipping dept and the rest is
thrown out as scrap. time for rework follows expo
(45)min independent of the part type. The system is
run for 4 8 hr shifts or 1920 min
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Travelers arrive at the main entrance door of an airline
terminal according to an EXPO Interarrival time ofmean 1.6 min, with the first arrival at time 0.
The travel time from entrance to check in is UNIF
distributed between 2 and 3 min. at the check incounter travelers wait in a single line until one of the
five agents is available to serve them.
Check in time follows a WEINB distribution with
parameters (7.76, 3.91). upon completion of their check
in they are free to travel to their gates., simulation run
time is 16 hrs .43
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Data IAT passengers= EXPO(1.6)min
Travel time to check in=UNIF (2,3)min Check in time =WIENB(7.76,3.91) min Simulation run time =16 hrs First arrival time =0 min
Find:- avg time in system, no of passengers completing check
in and avg length of check in Q
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The emergency room of a small hospital operates
around the clock. It is staffed by three receptionists at
the reception office, and two doctors on the premises,
assisted by two nurses. However, one additional doctor
is on call at all times; this doctor is summoned when the
patient workload up-crosses some threshold, and is
dismissed when the number of patients to be examinedgoes down to zero, possibly to be summoned again
later.
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Patients arrive at the emergency room according to a
Poisson process with mean interarrival time of 10 minutes.
An incoming patient is first checked into the emergency
room by a receptionist at the reception office. Check-in
time is uniform between 6 and 12 minutes. Since critically ill
patients get treatment priority over noncritical ones, each
patient first undergoes triage in the sense that a doctordetermines the criticality level of the incoming patient in
FIFO order.
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The time spent to reach the treatment room is uniform
between 1 and 3 minutes and the treatment time by a
nurse is uniform between 3 and 10 minutes.
Once treated by a nurse, a noncritical patient waits FIFO
for a doctor to approve the treatment, which takes a
uniform time between 5 to 10 minutes., all patients wait
FIFO for an available doctor, but critical patients aregiven priority over noncritical ones.
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Following treatment by a doctor, all patients are
checked out FIFO at the reception office, which takes
a uniform time between 10 and 20 minutes, following
which the patients leave the emergency room.
To estimate the requisite statistics, the hospital
emergency room was simulated for a period of 1 year.
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Model:- 2 segment ER segment, on call doc segment
Data:- receptionist=3, doc=2, on call doc= 1, nurse= 2
IAT patient=Poisson(10)min
Check in time Patients=unif(6,12)min Triage Time= tri(3,5,15)min
Critical patients =40%
Treatment time for P crit=unif(20,30)min
Travel time for p non cri= unif(1,3)min
Treatment time for p non cri=unif(3,10)min
Waiting time for all patient= unif (5,10)min
Check out time for all patient= unif (10,20)min
Simulation length= 1 year
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Modeling Production Lines :-resource allocation
problems- workload allocation and buffer capacity,
productivity improvement measures, Modeling
Machine Failures.
Modeling Transportation Systems:-Designing new
traffic routes and alternate routes to satisfy demand
for additional road capacity, or eliminating bottlenecks
and congestion points in existing routes, Designingtraffic patterns on the factory floor, Designing port
facilities and material handling systems.
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Modeling Computer Information Systems:-
Modeling Supply Chain Systems:-Customer service
levels, Average inventory levels and backorder
levels, Rate and quantity of lost sales, Inventory
management segment, Demand management
segment.
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The manufacturing facility is a production line composed
of manufacturing stages consisting of workstations with
intervening buffers to hold product flowing along the line.
push regime:- where little attention is given to the
finished-product inventory
pull regime:- where the process only produces in response
to specific demands
storage limitations in workstations give rise to a
bottleneck phenomenon, involving both blocking and
starvation
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Space limitations in a downstream workstation can,
therefore, cause stoppages at upstream
workstations a phenomenon known as blocking.
some workstations may experience idleness due tolack of job flow from upstream workstations. This
phenomenon is called starvation
starvation tends to propagate forward to
successive workstations located downstream in the
production line.
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Blocking and starvation are, in fact, the flip sides of
a common phenomenon and tend to occur
togethera bottleneck workstation
MODELS OF PRODUCTION LINES
Productivity losses are potentially incurred
whenever machines are idle (blocked or starved)
due to machine failures or bottlenecks originatingfrom excessive accumulation of inventories
between workstations.
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Design problems in production lines are primarily
resource allocation problems. workload allocation and buffer capacity allocation for a given set of
workstations with associated processing times
Performance analysis of production lines strives to
evaluate their performance measures as function of a
set of system parameters. The most commonly used
performance measures follow: Throughput, Average inventory levels in buffers Downtime probabilities Blocking probabilities at bottleneck workstations Average system flow times (also called manufacturing lead times)
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Consider a generic packaging line for some product,
such as a pharmaceutical plant producing a
packaged medicinal product, or a food processing
plant producing packaged foods or beverages.
The line consists of workstations that perform the
processes of filling, capping, labeling, sealing, and
carton packing. Individual product units will be
referred to simply as units.
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We make the following assumptions:
The filling workstation always has material in frontof it, so that it never starves. 2. The buffer space
between workstations can hold at most five units. 3.
A workstation gets blocked if there is no space in
the immediate downstream buffer (manufacturing
blocking).4. The processing times for filling,
capping, labeling, sealing, and carton packing are
6.5, 5, 8, 5, and 6 seconds, respectively.
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Note that these assumptions render our packaging
line a push-regime production. line. To keep matters
simple, no randomness has been introduced into the
system, that is, our packaging line is deterministic. It is
worthwhile to elaborate and analyze the behavior of
the packaging line understudy.
The first workstation (filling) drives the system in
that it feeds all downstream workstations with units.
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Clearly, one of the workstations in the line is the
slowest . The throughput of that workstation thencoincides with the throughput of the entire packaging
line.
Furthermore, workstations upstream of the slowest
one will experience excessive buildup of WIP inventory
in their buffers. In contrast, workstations downstream
of the slowest one will always have lightly occupied or
empty WIP inventory buffers.
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machine failures of various kinds constitute an
important source of idleness and variability . efficient
operation requires that downtimes be minimized, since
these represent loss of production time.
Failures that occur while machines are actually
processing jobs are called operation dependent and the
new breed of highly computerized machines may fail atany time, regardless of machine status. Such failures
are called operation independent
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Suppose that Filling Process in the packaging line
model of fails randomly and that it needs an
adjustment after every 250 departures from the
workstation.
Assume that uptimes are exponentially distributed
with a mean of 50 hours, while repair times are
uniformly distributed between 1.5 hours to 3 hours.
Also, the aforementioned adjustment time is uniformly
distributed between 10 minutes to 25 minutes.
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Assume further that Packing Process can also experience
random mechanical failures, and downtimes are triangularly
distributed with a minimum of 75 minutes, a maximum of 2
hours, and a mode at 90 minutes.
The corresponding uptimes are exponentially distributed
with a mean of 25 hours. Finally, assume that random failures
occur only while the machines are busy (operation-
dependent failures).
We shall refer to the modified packaging line model as the
failure-modified model
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how failures in the failure-modified model are
specified in a dialog spreadsheet for the Failure
module from the Advanced Process template
panel. Arena provides a mechanism for defining
resource states and for linking them to failures/
stoppages in the form of the StateSet spreadsheet
module from the Advanced Process template
panel.
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Monte Carlo simulation is an invaluable tool for
studying transportation systemsand solving their
attendant problems
Some common examples are listed below: Designing new traffic routes and alternate routes to satisfy
demand for additional road capacity, or eliminatingbottlenecks and congestion points in existing routes byappropriate placement of traffic lights and tollbooths.
Designing traffic patterns on the factory floor, includingtransporters and conveyors, for efficient movement of rawmaterial and product.
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The PickStation module allows entities to select a
destination Station module using a selection
criterion, such as the minimal or maximal queue size,
number of busy resource units, or an arbitraryexpression.
Alternatively, an entity can be endowed with an
itinerary using the Sequence module to specify a
sequence of Station modules
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A Job Shop producing 3 types of Gears; G1, G2, G3. Job
Shop consists of Arrival Dock, Milling , Drilling , Paint
Shop, Polishing Area, Shop Exit
Gear Jobs arrive in batches of 10 units. Their inter-arrival
times are uniformly distributed between 400 and 600
minutes. Of arriving batches, 50% are G1, 30% are G2, 20%
are G3.
Each gear type has different operation sequence. Gears
are transported by Two trucks running at a constant
speed of 100 feet/minute. Each truck can carry only one
gear at a time.73
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Transport Procedure:
The Transport starts from Arrival Dock When a job is complete at a location, the gear is placed into
an output buffer. A transport request is made for a truck, and the gear waits
for the truck to arrive.
Among the two trucks, the preference will be for the onewhich is closest to the requested location.
The transported job is placed in the input buffer of nextstation.
After all operations, the finished gear departs fromthe job shop via the Shop Exit.
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Assumptions:- Transporter (Truck) speed is same for both loaded and
empty. The freed transporter stays at the destination station until
requested by another station. The Job Shop works for 24 hours a day in 3 shifts at 8 hours
each.
Find:-
Gear flow time
Gear delays at operation location Resource utilization Improvements
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This example illustrates bulk port operations, using the notions
of station, entity routing among stations, entity pick-up and
drop-off by another entity, and the control of entity
movements using logical gating. It concerns a bulk material
port, called Port Tamsar, at which cargo ships arrive and wait
to be loaded with coal for their return journey. Cargo ship
movement in port is governed by tug boats, which need to be
assigned as a requisite resource. The port has a single berth
where the vessels dock, and a single ship loader that loads the
ships.
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A schematic representation of the layout of Port Tamsar
is depicted in Figure 13.2. Port Tamsar operates
continually 24 hours a day and 365 days a year. The
annual coal production plan calls for nominal
deterministic ship arrivals at the rate of one ship every
28 hours. However, ships usually do not arrive on time
due to weather conditions, rough seas, or otherreasons, and consequently, each ship is given a 5-day
grace period commonly referred to as the lay period.
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We assume that ships arrive uniformly in their lay
periods and queue up FIFO (if necessary) at an offshoreanchorage location, whence they are towed into port by
a single tug boat as soon as the berth becomes available.
The tug boat is stationed at a tug station located at a
distance of 30 minutes away from the offshore
anchorage.
Travel between the offshore anchorage and the berth
takes exactly 1 hour.
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We assume that there is an uninterrupted coal supply to
the ship loader at the coal-loading berth, and that ship
loading times are uniformly distributed between 14 and
18 hours.
Once a ship is loaded at the berth, the tug boat tows it
away to the offshore anchorage, whence the boat
departs with its coal for its destination.
Departing vessels are accorded higher priority in seizing
the tug boat.
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An important environmental factor in many port
locations around the world is tidal dynamics. Cargo shipsare usually quite large and need deep waters to get into
and out of port.
Obviously, water depth increases with high tide and
decreases with low tide, where the time between two
consecutive high tides is precisely 12 hours.
We assume that ships can go in and come out of port
only during the middle 4 hours of high tide. Thus, the
tidal window at the port is closed for 8 hours and open
for 4 hours every 12 hours.82
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We wish to simulate Port Tamsar for 1 year (8760
hours) to estimate berth and ship loader utilization,
as well as the expected port time per ship. We
mention parenthetically that although a number of
operating details have been omitted to simplify the
modeling problem, the foregoing description is
quite realistic and applicable to many bulk material
ports and container ports around the world.
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An Arena model of Port Tamsar consists of four
main segments: (1) ship arrivals, (2) tugboat
operations, (3)coal-loading operations at the berth,
and (4) tidal window modulation. These will be
described next in some detail along with
simulation results.
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2
3
4
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This example concerns a transportation system
consisting of a toll plaza on the New Jersey Turnpike,and aims to study the queueing delays resulting from
toll collection.
The toll plaza consists of two exact change (EC) lanes,
two cash receipt (CR) lanes, and one easy pass (EZP)
lane. Arriving vehicles are classified into three groups as
follows: 1. Fifty percent of all arriving cars go to EC lanes, and their
normal service time distribution is Norm(4.81, 1.01).
h f ll l d h
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2. Thirty percent of all arriving cars go to CR lanes, and theirservice time distribution is 5 Logn(4.67, 2.26).
3. Twenty percent of all arriving cars go to EZP lanes, and their
service time distribution is 1.18 4.29 Beta(2.27, 3.02).
To simplify matters, we assume that an incoming car
always joins the shortest queue in its category (EC, CR,
or EZP).
We further assume that no jockeying between queues
takes place. That is, once a car joins a queue in front of a
tollbooth, it never switches to another queue.
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Traffic congestion is distinctly non stationary, varying
widely by time of day. As expected, traffic is heavierduring the morning rush hour (6 A.M.9 A.M.) and the
evening rush hour (4 P.M.7 P.M.), and tapers off during
off-peak hours.
Table 13.1 summarizes vehicle interarrival time
distributions over each 24-hour period. The number of
operating cash receipt booths varies over time.
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Since such booths must be manned, and therefore are
expensive to operate, one of them is closed during the
off-peak hours.
Only during morning and evening rush hours do all
cash receipt booths remain open.
Typical performance analysis objectives for the toll
plaza system address the following issues:
What would be the impact of additional traffic on car
delays?
Would adding another booth markedly reduce waiting
times?89
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Could some booths be closed during light traffic hours without
appreciably increasing waiting times?
What would be the impact of converting some cash receipt
booths to exact change booths or to easy pass booths?
How would waiting times be reduced if both cash receipt booths
were to be kept open at all times?
Of course, additional issues may be specific to
particular toll plazas under study, but in our case we
wish to address the last issue in the list, using the
performance metrics of average time to pass through
the system and booth utilization.
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The model can be decomposed into the following
segments: creation of car entities from the appropriatedistributions over various time periods, dispatching a
car to the appropriate tollbooth with the shortest
queue, and serving incoming cars.
To this end, we use the Set construct to facilitate
modeling of module sets (model components) with
analogous logic (e.g., multiple tollbooths).
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Simulation Modeling and ArenaJanuary 2009ISBN 978 0 470 09726 7
Charu Chandra, University of Michigan - Dearborn Translations of Simulation with Arena, 3rdEdition
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ISBN: 978-0-470-09726-7Manuel D. Rossetti, Associate Professor of IndustrialEngineering, University of Arkansas,Department of Industrial Engineering .Healthcare Operations ManagementMay 2008ISBN 13: 978-1-56793-288-1Daniel B. McLaughlin, DirectorCenter for BusinessExcellence in the Opus College of Business at theUniversity of St. ThomasJulie M. Hays, PhDSimulation Modeling and Analysis with Arena
Academic PressISBN-13: 978-0-12-370523-5Tayfur Altiok, Professor, Department of IndustrialEngineering, Rutgers University altiok@rci.rutgers.eduBenjamin Melamed, Professor, Department ofManagement Science and Information ystems,Rutgers University melamed@rbs.rutgers.edu Process Analysis and Improvement: Tools andTechniquesMcGraw-Hill IrwinISBN: 0072857129 Marvin S Seppanen, Productive SystemsSameer Kumar, University of St. Thomas, Minneapolis
EditionDr. Soemon Takakuwa (Japanese Translator)January 2005. McGraw Hill Publisher.ISBN 4-339-08246-5Moon Il Kyeong (Korean Translator)Kyobo Book Centre Publisher. January 2005ISBN 8970855122 Applied Simulation ModelingISBN: 0534381596Copyright year: 2003 Andrew Seila - University of GeorgiaVlatko Ceric - University of Zagreb
Pandu Tadikamalla - University of Pittsburgh Introduction to Modeling and Simulation ofSystems with ArenaVisual BooksCopyright 2003 PortugueseISBN 85-7502-046-3
93
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