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3.2.1 introduction to simulation

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Vishal Tidake
Vishal Tidake

Basics of Simulation

Data & Analytics
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www.sanjivanimba.org.in Unit No.3. DECISION SCIENCE Presented By: Dr. V. M. Tidake Ph. D (Financial Management), MBA(FM), MBA(HRM) BE(Chem) Dean, EDP & Associate Professor MBA 1 Sanjivani College of Engineering, Kopargaon Department of MBA www.sanjivanimba.org.in
www.sanjivanimba.org.in 302-DECISION SCIENCE Unit No.3 Marko Chain & Simulation 3.2.1 Introduction to Simulation Presented By: Dr. V. M. Tidake Ph. D (Financial Management), MBA(FM), MBA(HRM) BE(Chem) Dean EDP & Associate Professor MBA 2 Sanjivani College of Engineering, Kopargaon Department of MBA www.sanjivanimba.org.in
www.sanjivanimba.org.in MARKOV CHAIN & SIMULATION  At the End of the Session Student will be able to understand- A. Introduction to Simulation
www.sanjivanimba.org.in SIMULATION Simulation is a methodology of development of a model of a real system and then performing experiments on it with a view to predict the behavior of a system over a period of time. Examples: a. Predicting the market share for a product for next financial year. b. Predicting the Level of sales based on past sales figure.
www.sanjivanimba.org.in PROCESS OF SIMULATION 1. Problem Definition. 2. Development of Appropriate Model of real system by collecting enormous amount of appropriate data. 3. Experimentation (Manipulation) with the model. 4. Analysis and Interpretation of the results in order to upgrade model performance if required.
www.sanjivanimba.org.in SIMULATION ADVANTAGES: 1. Useful for analyzing complex systems where traditional mathematical models can not be used. 2. Easy to Use, Flexible and Less Expensive. 3. Risk of interference with real world gets avoided. 4. Interactive effect of Individual component can be studied.
www.sanjivanimba.org.in SIMULATION DISADVANTAGES: 1. Does not give optimal solutions, just descriptive in nature. 2. Complicated and Time Consuming Task. 3. No Precision. 4. Only Uncertain Situations gets evaluated.
www.sanjivanimba.org.in ELEMENTS OF SIMULATION MODEL SYSTEM INPUT: It is the logic and rules which governs the Model. RANDOM NUMBER GENERATION: Used to Simulate Model. WORK DATA SHEET: Is the Sheet used to Record the Results of Simulation and are used to obtain further Results.
www.sanjivanimba.org.in STEPS IN MONTE CARLO SIMULATION 1. Identify the Input variables, collect data on them and write Probability Distribution for them. This represents Simulation Model. 2. Rewrite the Cumulative Probability Distribution. 3. Identify the Random No. Intervals corresponding to these cumulative Probability Figures. (00 to 99 or 000 to 999)
www.sanjivanimba.org.in STEPS IN MONTE CARLO SIMULATION 4. Prepare a Table for Random Numbers and the expected/Simulated values of the Input variables. 5. Generate ‘n’ random Numbers from the Random No. Table (if not given). For each random No. identify its Random Number Interval. And then the corresponding value of the input variable is the simulated value. 6. Process this Simulated information and then Summarize and Interpret the Data.
www.sanjivanimba.org.in EXERCISE  State and Explain Simulation in detail.
www.sanjivanimba.org.in For More Details Contact Dr. V M Tidake tidkevishal@gmail.com tidkevishalmba@sanjivani.org.in
www.sanjivanimba.org.in Thank You

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5.2.6 case 1 poisson probability distribution5.2.6 case 1 poisson probability distribution
5.2.6 case 1 poisson probability distribution
 
5.2.4 case 1 binomial probability distribution
5.2.4 case 1 binomial probability distribution5.2.4 case 1 binomial probability distribution
5.2.4 case 1 binomial probability distribution
 
5.10 case 7 probability
5.10 case 7 probability5.10 case 7 probability
5.10 case 7 probability
 
5.9 case 6 probability
5.9 case 6 probability5.9 case 6 probability
5.9 case 6 probability
 
5.7 case 4 probability
5.7 case 4 probability5.7 case 4 probability
5.7 case 4 probability
 
5.5 case 2 probability
5.5 case 2 probability5.5 case 2 probability
5.5 case 2 probability
 

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3.2.1 introduction to simulation

  • 1. www.sanjivanimba.org.in Unit No.3. DECISION SCIENCE Presented By: Dr. V. M. Tidake Ph. D (Financial Management), MBA(FM), MBA(HRM) BE(Chem) Dean, EDP & Associate Professor MBA 1 Sanjivani College of Engineering, Kopargaon Department of MBA www.sanjivanimba.org.in
  • 2. www.sanjivanimba.org.in 302-DECISION SCIENCE Unit No.3 Marko Chain & Simulation 3.2.1 Introduction to Simulation Presented By: Dr. V. M. Tidake Ph. D (Financial Management), MBA(FM), MBA(HRM) BE(Chem) Dean EDP & Associate Professor MBA 2 Sanjivani College of Engineering, Kopargaon Department of MBA www.sanjivanimba.org.in
  • 3. www.sanjivanimba.org.in MARKOV CHAIN & SIMULATION  At the End of the Session Student will be able to understand- A. Introduction to Simulation
  • 4. www.sanjivanimba.org.in SIMULATION Simulation is a methodology of development of a model of a real system and then performing experiments on it with a view to predict the behavior of a system over a period of time. Examples: a. Predicting the market share for a product for next financial year. b. Predicting the Level of sales based on past sales figure.
  • 5. www.sanjivanimba.org.in PROCESS OF SIMULATION 1. Problem Definition. 2. Development of Appropriate Model of real system by collecting enormous amount of appropriate data. 3. Experimentation (Manipulation) with the model. 4. Analysis and Interpretation of the results in order to upgrade model performance if required.
  • 6. www.sanjivanimba.org.in SIMULATION ADVANTAGES: 1. Useful for analyzing complex systems where traditional mathematical models can not be used. 2. Easy to Use, Flexible and Less Expensive. 3. Risk of interference with real world gets avoided. 4. Interactive effect of Individual component can be studied.
  • 7. www.sanjivanimba.org.in SIMULATION DISADVANTAGES: 1. Does not give optimal solutions, just descriptive in nature. 2. Complicated and Time Consuming Task. 3. No Precision. 4. Only Uncertain Situations gets evaluated.
  • 8. www.sanjivanimba.org.in ELEMENTS OF SIMULATION MODEL SYSTEM INPUT: It is the logic and rules which governs the Model. RANDOM NUMBER GENERATION: Used to Simulate Model. WORK DATA SHEET: Is the Sheet used to Record the Results of Simulation and are used to obtain further Results.
  • 9. www.sanjivanimba.org.in STEPS IN MONTE CARLO SIMULATION 1. Identify the Input variables, collect data on them and write Probability Distribution for them. This represents Simulation Model. 2. Rewrite the Cumulative Probability Distribution. 3. Identify the Random No. Intervals corresponding to these cumulative Probability Figures. (00 to 99 or 000 to 999)
  • 10. www.sanjivanimba.org.in STEPS IN MONTE CARLO SIMULATION 4. Prepare a Table for Random Numbers and the expected/Simulated values of the Input variables. 5. Generate ‘n’ random Numbers from the Random No. Table (if not given). For each random No. identify its Random Number Interval. And then the corresponding value of the input variable is the simulated value. 6. Process this Simulated information and then Summarize and Interpret the Data.
  • 11. www.sanjivanimba.org.in EXERCISE  State and Explain Simulation in detail.
  • 12. www.sanjivanimba.org.in For More Details Contact Dr. V M Tidake tidkevishal@gmail.com tidkevishalmba@sanjivani.org.in