simulation

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SIMULATION Presented by Sunaina Dubey Mba 3 rd sem

Contents of presentation:

Contents of presentation Definition Steps in simulation Singnificance of simulation Techniques of simulation Example –problem of Monte carlo simulation and its solution

simulation:

simulation “Simulation is the process of designing a model of a real system and conducting experiments with the model for the purpose of understanding the behaviour for the operation of the system”

General meaning :

General meaning In general terms simulation involves developing a model of some real phenomenon and then performing an experiment on the model evolved.It is a descriptive and not optimisting techniques.

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Steps for simulation Formulation of problem Introduce important varibles decision rules of system parameters Construct simulation model Validate the model Design experiments Perform simulation Is simulation process is complete? Select the best course of action Modify the modele by changing the input data

Steps in simulation :

Steps in simulation Formulation of problem Introduce important variables decision rules of system parameters. Construct simulation model Validate the model Design experiments Perform simulation Is simulation problem is complete Select the best course of action

Singnificance of simulation:

Singnificance of simulation To solve cumbersome problem Experiment Study the long term effect To best proposed analytical solution Stability Generation of data Time saving Last resort

Techniques of simulation :

Techniques of simulation Monte carle simulation Simulation and buisness situation Simulation and inventory control Simulation and financial decision

Mante Carlo simulation:-Mante carlo method may be applied when a system elements that exhibit chance in their behavior so monte carlo is an experiment on the chance:

Mante Carlo simulation :- Mante carlo method may be applied when a system elements that exhibit chance in their behavior so monte carlo is an experiment on the chance Steps:- Establishing probabilty distribution Comulative probability distribution Setting random number intervals Generating random no

Example :

Example QUE:- Over 100 days period the daily demands of a certain commodity shown the following frquency distribution pattern Daily demand 0 1 2 3 4 5 No. of days 10 20 40 20 6 4 Total 100 Using the given data simulate a ten day sequence of the demands values

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Daily demand Probabilty =no of days/total no of days Cumulative probability Rendom no assignment 0 10/100=0.10 0.10 00-09 1 20/100=0.20 0.30 10-29 2 40/100=0.40 0.70 30-69 3 20/100=0.20 0.90 70-89 4 6/100=0.06 0.96 90-95 5 4/100=0.04 1.00 96-99 How the random no.assignment has been made is very simple to figure out (on the basis of cumulative frequency) Solution:-

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Day number Generated random demands Generating demand 1 67 2 2 84 3 3 01 0 4 77 3 5 90 4 6 14 1 7 15 1 8 74 3 9 44 2 10 77 3 Total=22 Average daily demand =22/10 =2.2 answer

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