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Basics of Monte Carlo Simulation

Abstract

Simulation is one of the most widely used probabilistic modeling tools in industry. It is used for the analysis of existing systems and for the selection of hypothetical systems. For example, suppose a bank has been receiving complaints from customers regarding the length of time that customers are spending in line waiting at the drivein window. Management has decided to add some extra windows; they now need to decide how many to add. Simulation models can be used to help management in determining the number of windows to add. Even though the main focus of this textbook is towards building analytical (as opposed to simulation) models, there will be times when the physical system is too complicated for analytical modeling; in such a case, simulation would be an appropriate tool. The idea behind simulation, applied to this banking problem, is that a computer program would be written to generate randomly arriving customers, and then process each customer through the drive-in facility.

Keywords

  • Random Number
  • Random Number Generator
  • Defective Item
  • Standard Normal Random Variate
  • Random Number Seed

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Richard M. Feldman .

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Feldman, R.M., Valdez-Flores, C. (2010). Basics of Monte Carlo Simulation. In: Applied Probability and Stochastic Processes. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05158-6_2

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  • DOI: https://doi.org/10.1007/978-3-642-05158-6_2

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