Simulation

Objectives

The course aims to provide students with knowledge on methodologies, models and techniques for discrete simulation. As a means to assist students in systems modeling and its simulation it is used a commercial software. This course therefore has a strong practical component of formulation, modeling and solving problems in the laboratory, being used computers. The adequate modeling of a system allows to make the simulation of its operation, in a virtual environment, and to evaluate their performance considering different scenarios and different management policies.

It is intended that at the end of the course students have acquired the skills to simulate part of an operations management system by building a mathematical model that comes as close as possible to representing the reality of the system. Additionally it is intended to develop skills that allow them to select the methodologies and techniques appropriate to the particular system both with respect to the modeling and the analysis of the results of the simulation.

It is intended also that students are able to develop a critical sense regarding the system performance obtained from the simulation results analysis.

General characterization

Code

2309

Credits

6.0

Responsible teacher

Ana Paula Ferreira Barroso

Hours

Weekly - 4

Total - 56

Teaching language

Português

Prerequisites

Available soon

Bibliography

Kelton W.D., Sadowski R.P. e Zupick N.B. (2015) Simulation with ARENA (6ª ed.), McGraw-Hill International Edition, New York.

Law A.M. e Kelton W.D. (2007) Simulation Modeling and Analysis, McGraw-Hill International Edition, New York.

Banks J. (1998) Handbook of Simulation, John Wiley & Sons, Atlanta.

Banks J. (2001) Discrete-Event System Simulation (3ª ed.), Prentice-Hall, New Jersey.

Chung C.A. (2004) Simulation Modeling Handbook. A Practical Approach, CRC Press, Boca Raton.

Pidd M. (1994) Computer Simulation in Management Science, John Wiley & Sons, Singapore.

Teaching method

The curricular unit is taught in lectures and labs.

In lectures, with a charge of 2 hours a week, key concepts, methodologies, and models are discussed based on examples and case studies. Oral questions are frequently made for prerequisite control, knowledge assessment and stimulate students’ participation.

In labs, with a charge of 2 hours a week, the experimental method is adopted. Exercises are solved allowing students to both gain a deeper understanding of the topics and develop reasoning skills. Students are challenged with multifaceted problems which should be solved in team. The way of solving problems is analyzed and discussed in class.

Classes are prepared by students based on book chapters and scientific articles, as well as group or individual solving of exercises. Active methods are used. Some attention is given to the oral presentation and written projects.

Evaluation method

The course grading is based on closed-book tests (T1 and T2) and a project (in a team, Trb-Gr), with a weighting of 70 and 30% in the final grade, respectively.

Final grade =  0,35 T1 + 0,35 T2 + 0,30 Trb-Gr

Dates:  T1 - 27/out   e   T2 - 05/dec  (proposals)

Assessment elements: rounded to the hundredths.

To be exempted from the final exam, the student must assure a mark equal or above 9.5 values on average of closed-book tests.

The student is excluded from the final exam if he / she is not present in at least 9 lectures and 9 laboratory sessions.

Subject matter

  1. Introduction and fundamental simulation concepts. Simulation model components.
  2. Methodology of a simulation study. Formulation of the problem. Simulation model. Verification and validation of models. Experimentation and analysis. Randomness and replication of the output of simulation system.
  3.  Modeling operations, systems, transporters and conveyor belts.
  4. Statistical issues of the input of model. Fitting input distributions from collected data. Non-stationary arrival processes.
  5. Statistical analysis of output from Terminating simulations. Comparative analysis of scenarios / alternatives. Statistical comparison of two scenarios. Definition of confidence intervals. Statistical analysis of output from Steady-State simulations. Warm-up period. Truncated replications.
  6. Random-number generation, random variables generation and variance reduction.

Programs

Programs where the course is taught: