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 the Arena 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.
Ana Paula Ferreira Barroso, António Carlos Bárbara Grilo
Weekly - 4
Total - 61
Law A.M. e Kelton W.D. (2007) Simulation Modeling and Analysis, McGraw-Hill International Edition, New York.
Kelton W.D., Sadowski R.P. e Zupick N.B. (2015) Simulation with ARENA (6ª ed.), 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.
In lectures the expositive method is adopted to present concepts, methods and models. Oral questions are frequently made for prerequisite control, knowledge assessment and stimulate students’ participation.
In laboratory sessions the experimental method is adopted. Active methods are used. Students are challenged with multifaceted problems which should be solved in team. Also, case studies are analyzed and discussed in class.
The course grading is based on closed-book tests (T1 and T2) and projects (1 individual, Trb-I, and the other in a team, Trb-Gr), with a weighting of 60 and 40% in the final grade, respectively.
Final grade = 0,30 T1 + 0,30 T2 + 0,10 Trb-I + 0,30 Trb-Gr
T1: XX oct; T2: XX dec; Trb-I: XX/XX oct
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, and the grade of TRB-Gr does not exceed 9.5 values.
- Introduction and fundamental simulation concepts. Simulation model components.
- 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.
- Introduction to Arena Software. Modeling operations, systems, transporters and conveyor belts.
- Statistical issues of the input of model. Fitting input distributions from collected data. Non-stationary arrival processes.
- 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.
- Random-number generation, random variables generation and variance reduction.
Programs where the course is taught: