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**Course code number and name:**300IGI005, Simulation and Waiting Queues.**Credits and contact hours:**3 credit hours, 4 hours per week**Course coordinator:**Germán Córdoba**Prerequisites:**Stochastic Processes (300IGQ004).**Type of course:**Required.

- Simulation and System Analysis with Promodel, E. García, H. García, L. Cárdenas, 2006

**Supplemental materials**

- Guide for Practices and Simulation Models with Promodel, G. Córdoba, 2011.
- Simulation Using Promodel, 2nd Edition, C. Harrel, B. Ghosh, R. Bowden, 2003.
- Promodel 2011 Users Guide, Promodel Corporation, 2011.

This course presents the mathematical, statistical and logical techniques of the simulation tools that are used to design models that represent the dynamic and random behavior of realistic systems of manufacture, logistics and service. The objective of those models is to understand, analyze, design, improve and solve problems of the systems under study and determine how they respond to changes in their structure, environment and operating conditions.

- To recognize simulation as a tool of modern Industrial Engineering.
- To apply statistical tools to systems with random variables.
- To identify the uses of simulation models and obtain uniform random numbers.
- To apply Monte Carlo simulation to the design and use of tabular models.
- To select and apply the techniques for the generation of random values with standard distributions.
- To verify and validate the results of a simulation model.
- To use specialized software tools (Promodel, Flexim) to design and analyze simulation models.

Student Outcomes | |||||||||||
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A | B | C | D | E | F | G | H | I | J | K | |

Relevance | 3 | 2 | 3 | 3 | 3 | 3 |

1: low relevance; 2: medium relevance; 3: high relevance.

- Simulation in today’s globalized context.
- Random variables: types (discrete, continuous), probability distribution functions with discrete and continuous random variables.
- Standard distribution functions with goodness-of-fit proofs, chi-square proof.
- Random numbers: features and relationship with the uniform and continuous distribution function. Physical methods for the generation of random numbers.
- Statistical tests of generated random numbers. Proofs of goodness of fit; normality, mean, variance.
- Random sampling and simulated sampling.
- Tabular models with the Monte Carlo method. Application to prototype cases: queues, manufacturing, inventories.
- Methods to generate random values with standard distributions.
- Techniques to generate random values in tabular models. Case and examples in Excel.
- Terminal and non-terminal simulations.
- Structure, features and philosophy of the simulation software. Graphic and animation interfaces.
- Use of networks, resources and routing rules in simulation models and representation of process flows.
- Design of models with groups.
- Steps to follow in a simulation project: planning, problem definition.