Chapter Report Simulation Quanti

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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