Systems and Simulation
Objectives
At the end of the Systems and Simulation course, students should be able to:
- Define production systems, production factors, processes, operations and products.
- Have a basic knowledge to identify and recognize the role of simulation as a tool to support decision making, both in improving systems performance and in designing systems.
- Model the input of a simulation model from system data.
- Carry out a simulation output analysis process.
- Conduct or lead a simulation study
General characterization
Code
12852
Credits
3.0
Responsible teacher
António Carlos Bárbara Grilo, Helena Maria Lourenço Carvalho Remígio
Hours
Weekly - 2
Total - 32
Teaching language
Português
Prerequisites
It is an introductory course unit, there are no requirements to mention.
Bibliography
Krajewski L. J., Malhotra M. K. & Ritzman L. P. (2019). Operations Management. Processes and Supply Chains (12th ed.), Pearson Education Limited, Global edition, Harlow, England.
Law A.M. & Kelton W.D. (2007). Simulation Modeling and Analysis, McGraw-Hill International Edition, New York.
Kelton W.D., Sadowski R.P. & Zupick N.B. (2015). Simulation with ARENA (6th ed.), McGraw-Hill International Edition, New York.
Banks J. (2001). Discrete-Event System Simulation (3rd ed.), Prentice-Hall, New Jersey.
Chung C.A. (2004). Simulation Modeling Handbook. A Practical Approach, CRC Press, Boca Raton.
Teaching method
The teaching methodology will be based on an active method where students are responsible for carrying out a set of learning activities carried out autonomously. These activities are duly identified weekly and the study materials to be used in CLIP. In addition, the expository method will be applied to consolidate theoretical concepts, as well as the resolution of theoretical-practical exercises.
Additionally, 2 practical activities will be carried out in the classes on September 27 and October 18. Additionally, a Project is proposed that will be developed throughout the semester by carrying out a set of 4 activities
Evaluation method
In this UC, "frequency" is not considered. The Final Grade (NF) will be given by 3 evaluation components (each one measured on a scale of 0-20 values) according to the following formula:
NF = 0.3 Individual Test + 0.2 Individual Presentation + 0.5 Project
Individual Test – individual test carried out online, with theoretical questions and exercises. Expected duration of 1 hour. This assessment element has a minimum grade of 9.5 val for approval at the UC.
Individual Presentation – presentation of a theme chosen by the student. In the development of the theme, it is mandatory to cite at least 1 scientific article. The presentation will have a maximum duration of 8 min, and will be held in the classroom.
Project - concerns the development of a simulation model. It will be carried out in groups of 3 students. There will be 3 activities in the classroom and 1 activity in autonomy. The Project grade will be given by the evaluation of the 4 Activities. Each Activity is evaluated taking into account the results obtained (i.e. achievement of the proposed objectives, as well as the sophistication and adequacy of the approach used) and the contribution of each element of the group to the realization of the Activity.
Project = (A1 + A2 + A3 + A4) /4
The score for each Activity is given by Ai = 0.8 Results + 0.2 Individual Contribution
Subject matter
1. Concepts of production systems, processes and operations.
2. Introduction to simulation. Concepts and simulation model components.
3. 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 a model.
4. Introduction to Arena software. Modeling operations of simple systems.
5. Statistical issues of the input of model. Fitting input distributions from collected data. Non-stationary arrival processes.
6. Statistical analysis of output from Terminating simulations. Comparative analysis of scenarios. Statistical comparison of two scenarios. Confidence intervals. Statistical analysis of output from Steady-State simulations. Warm-up period. Truncated replications.