Industrial Engineering and Management
ADVANCED SIMULATION MODELS
Description
Laboratory
4
Instructors
Luís Carlos Ferreira
Contents
1.Simulation as a tool in decision-making. Conceptual Modeling of simulation models.
2.Methodologies used in the approach to the modelling of Systems: Dynamics Systems (Identification of key concepts in the modelling of a system - Stocks, Flows, Variables, Feedbacks, Cycles; Graphic representation using syntax and semantics suited to system dynamics).
3.Discrete event simulation and the Python language (Simpy) for simulation design applied to various industrial situations. Testing and validating Simulation models
4. Notions of Statistics applied to simulation. Random number generation. Monte Carlo simulation. Management of Service and Queues. Waiting Theory. Queuing Systems (Arrivals, Queue, Attendance, Little's Law). The system One queue / One server: M/M/1. The One Queue / multiple servers: M/M/s. Deterministic systems: M/D/1. Examples of applications.
Learning Outcomes
This course has the following main objectives:
(A) Acquisition of a mind-set of critical reflection and the development of the capacity to research, synthesize, structure and present information in the area of Industrial Simulation.
(B) Acknowledgement of the multiplicity and complexity of issues related to industrial simulation, instilling the need for the continuous updating and broadening of knowledge and skills.
(C) Understand and analyse behaviour of a working system.
(D) Develop models that assist simulation projects.
(E) Discuss results and conclusions from simulation programs.
(F) Understanding how Dynamics of Systems can constitute a methodology of approach, which is very useful in the modelling of Systems.
(G) Understand how Discrete Simulation can be a very useful approach methodology for decision support.
(H) Understand how the Theory of Queuing can be a very useful approach methodology for decision support.