Lecture11 - MBAC6080 Decision Modeling and Applications...

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1 MBAC6080 Thomas Vossen Assistant professor of Operations Management Leeds School of Business University of Colorado Boulder, CO 80309-0419 Decision Modeling and Applications
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Lecture 11, 4-2-2008 2 MBAC6080 Agenda Simulation - Introduction - Using Crystall Ball - Output Analysis, Decision Tables, Multiple sources of uncertainty - Examples
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Lecture 11, 4-2-2008 3 MBAC6080 Simulation Simulation: a descriptive technique that enables a decision maker to evaluate the behavior of a model under various conditions. - Simulation models complex situations - Models are simple to use and understand - Models can play “what if” experiments - Extensive software packages available
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Lecture 11, 4-2-2008 4 MBAC6080 Analytic models: values of decision variables are the outputs. Simulation models: values of decision variables are the inputs. - Investigate the impacts on certain parameters when these values change. Simulation
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Lecture 11, 4-2-2008 5 MBAC6080 Analytic models - May be difficult or impossible to obtain. - Typically predict only average or steady-state behavior. Simulation models - Wide availability of software and more powerful PCs make implementation much easier than before. - More realistic random factors can be incorporated. - Easier to understand. Why Simulation?
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Lecture 11, 4-2-2008 6 MBAC6080 1. Identify the problem 2. Develop the simulation model 3. Test and Validate the model 4. Develop the experiments 5. Run the simulation and evaluate results 6. Repeat until results are satisfactory The Simulation Process
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Lecture 11, 4-2-2008 7 MBAC6080 Monte Carlo method: Probabilistic simulation technique used when a process has a random component Identify a probability distribution Setup intervals of random numbers to match probability distribution Obtain the random numbers Interpret the results “Monte Carlo” Simulation
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Lecture 11, 4-2-2008 8 MBAC6080 Random input factors: sales, demand, stock prices, interest rates, the length of time required to perform a task. Random performance measures: - Business profit within a time interval. - Average waiting time of a customer in a queuing system. Random input factors random performance measures. Major Components of Models
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Lecture 11, 4-2-2008 9 MBAC6080 Simulation A model that mimics the behavior of a (random) system Model Probabilistic Input (random number) Controllable Input Outcome Analysis r 1 , r 2 , …, r n
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Lecture 11, 4-2-2008 10 MBAC6080 How to Generate Random Numbers?
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Lecture 11, 4-2-2008 11 MBAC6080 How to Generate Random Numbers? The Monte Carlo Technique a technique for selecting numbers randomly from a probability distribution for use in a trial (computer run) of a simulation model. The basic principle similar to operation of gambling devices in casinos (such as those in Monte Carlo, Monaco).
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Lecture 11, 4-2-2008 12 MBAC6080 Example: Computer World demand data for laptops over a period of 100 weeks.
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Lecture11 - MBAC6080 Decision Modeling and Applications...

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