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SIMULATION MODELINGReported by: muriel jane monforte
CHAPTER 14
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Advantages and disadvantages:
Advantages:1. It is relatively straightforward and flexible.
2. Recent advances in software make some simulation models very easy todevelop.
3. It can be used to analyze large and complex real-world situations thatcannot be solved by conventional quantitative analysis models.
4. Simulation allows what-if types of questions.
5. Simulations do not interfere with the real-world system.
6. Simulation allows us to study the interactive effect of individual
components of variables to determine which ones are important.
7. Time compression is possible with simulation.
8. Simulation allows for the inclusion of real-world complications that mostquantitative analysis models cannot permit.
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Disadvantages1. Good simulation models for complex situations can be very
expensive.
2. Simulation does not generate optimal solutions to problems
as do other quantitative analysis techniques such as EOQ, LP ,or PERT.
3. Managers must generate all of the conditions and constraintsfor solutions that they want to examine.
4. Each simulation model is unique.
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Brief History
World War II
Monte Carlo simulation: originated with
the work on the atomic bomb. Used to simulate bombing raids. Given the
security code name Monte-Carlo.
Still widely used today for certain problems which are not analytically solvable (for
example: complex multiple integrals)
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What can be simulated?
Almost anything can
and
almost everything has...
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Applications:
COMPUTER SYSTEMS: hardware components, software
systems, networks, data base management, information
processing, etc..
MANUFACTURING: material handling systems, assembly
lines, automated production facilities, inventory control
systems, plant layout, etc..
BUSINESS: stock and commodity analysis, pricing policies,
marketing strategies, cash flow analysis, forecasting, etc..
GOVERNMENT: military weapons and their use, military
tactics, population forecasting, land use, health care
delivery, fire protection, criminal justice, traffic control, etc..
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Monte Carlo simulation
The basic idea in Monte CarloSimulation is to generate values forthe variables making up the model
being studied. There are a lot ofvariables in real world systems that areprobabilistic in nature that we want to
simulate.
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Examples of variables:
1. Inventory demand on a daily or weekly basis
2. Lead time for inventory orders to arrive
3. Times between machine breakdowns
4. Times between arrivals at a service facility
5. Service times
6. Times to complete project activities
7. Number of employees absent from work each day
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Harrys auto tire example
Harrys Auto Tire sells all types of tires, but a popular
radial tire accounts for a large portion of Harrys
overall sales. Recognizing that inventory costs can
be quite significant with this product, Harry wishes to
determine a policy for managing this inventory. Tosee what the demand would look like over a period
of time, he wishes to simulate the daily demand for a
number of days.
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Step 1:establishing probability distributions
Demand forTires
Frequency (days) Probability ofOccurence
0 10 10/200= 0.05
1 20 20/200= 0.10
2 40 40/200= 0.20
3 60 60/200= 0.30
4 40 40/200= 0.20
5 30 30/200= 0.15
200 200/200=1.00
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Step 2: building a cumulative probability distribution for each variable
DAILY DEMAND PROBABILITY CUMULATIVEPROBABILITY
0 0.05 0.05
1 0.10 0.15
2 0.20 0.353 0.30 0.65
4 0.20 0.85
5 0.15 1.00
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STEP 3: setting random number intervals
DAILYDEMAND
PROBABILITY CUMULATIVEPROBABILITY
INTERVAL OFRANDOMNUMBERS
0 0.05 0.05 01 to 05
1 0.10 0.15 06 to 152 0.20 0.35 16 to 35
3 0.30 0.65 36 to 65
4 0.20 0.85 66 to 85
5 0.15 1.00 86 to 00
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Step 4: generating random numbers
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SIMKIN HARDWARE STORE
Mark Simkin, owner and generalmanager of Simkin Hardware, wants tofind a good, low cost inventory policy
for one particular product: the Acemodel electric drill. Due to complexityof this situation, he has decided to use
simulation to help with this.
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Other simulation issues
Two other types of Simulation Models
1. Operational Gaming- refers to simulation involvingtwo or more competing players.
2. Ex.: military games, business games
3. Systems Simulation- similar to business gaming,allows users to test various managerial policies anddecisions to evaluate their effect on the operatingenvironment. Large system dynamics.
4. Ex.:corporate operations, national economy, hospitalor city govt. system
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Verification and validation
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- End -
And now
Any questions???
thanks
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