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UNIFEI

Universidade Federal de Itajubá – International

UNIFEI > Academic Programs > European Project Semester > Programs

Programs

 

Engineering Study Programs at UNIFEI
(Including orientation for Undergraduate Final Paper)

 

Project Management (45h)
Prof. João Batista Turrioni

Goal: Project management of a real problem using peer instruction

Topics:

  • Project Management
  • Roadmap of problem solution
  • Real projects
  • Communication

Reference:

  • Light, G.; Micari, M. (2013). Making scientists: six principles for effective college teaching
  • Novak, G; Patterson, E. (1999). Just in time teaching: Blending Active Learning with web technology
  • Mazur, E. (1999). Peer Instruction: A user´s Manual

 

Applied Statistics (45h)
Prof. João Batista Turrioni

Goal: This undergraduate level course provides an introduction to the basic concepts of probability, common distributions, statistical methods, and data analysis.
This course will use the statistical software program Minitab.

Topics:

  • Descriptive Statistics
  • Probability Distribution
  • Parameter Estimation
  • Hypothesis Testing
  • Analysis of Variance
  • Design of Experiments

Reference:

  • Montgomery, Runger and Hubele, Engineering Statistics.5th edition

 

Production Planning and Control (45h)
Prof. Fabio Favaretto

Goal: To know the basic principles of production planning using hierarchic methods and effective usage of production resources.

Topics:

  • Overview of production planning
  • Demand planning
  • Capacity planning
  • Aggregate production planning
  • Master plan scheduling
  • Materials requirements planning
  • Production Scheduling

Reference:

  • Slack, N. Brandon-Jones; A.; Johnston, R.; Betts, A.; (2011). Operations & Process Management, 4th ed., Trans-Atlantic Publications, Inc
  • Stevenson, W. J. (2014). Operations Management, McGraw-Hill Education

 

Six Sigma (45h)
Prof. Pedro Paulo Balestrassi

Goal: Development of a Six Sigma Project

Topics:

  • Define
  • Measure
  • Analysis
  • Improve
  • Control
  • DFSS

Reference:

  • Keller, P. (2010). Six Sigma Demystified, 2nd Edition
  • Cudney, E (2016). Design for Six Sigma: A Practical Approach through Innovation

 

Forecasting (45h)
Prof. Rafael Leme

Goal: This course provides a working knowledge of forecasting methods. Techniques in univariate forecasting using exponential smoothing, regression methods for time series data, stationary and non-stationary time series models for seasonal and non-seasonal time series data and model selection procedures are covered. The emphasis is on methods and the analysis of time series data using the Minitab statistical software.

Topics:

  • Introduction to Forecast
  • Statistical Background for Forecasting
  • Regression Analysis and Forecasting
  • Exponential Smoothing Methods
  • ARIMA

Reference:

  • Introduction to Time Series Analysis and Forecasting (Montgomery / Jennings / Kulahci)

 

Business Logistics (45h)
Prof. Renato Lima

Goal: Development of Project-Based Learning in a logistic/SCM subject

Topics:

  • Logistics / SCM
  • Logistic Service Level
  • Distribution Channels
  • Distribution Logistics
  • Transport Fundamentals
  • Storage and Handling
  • Reverse Logic

Reference:

  • Christhopher, M. Logistics & Supply Chain Management (5th Edition)

 

Simulation (45h)
Prof. José Arnaldo Barra Montevechi

Goal: In this course, we will present and discuss the simulation to discrete events.

Topics:

  • Introduction to simulation
  • Simulation terminology
  • Simulation modeling method
  • An introduction to discrete event simulation software
  • Case study

Reference:

  • Harrell; Ghosh; Bowden; “Simulation using promodel”, McGraw Hill, 2000
  • Harrel; Mott; Bateman; Bowden; Gogg; “System improvement using simulation”, Promodel Corporation, 1996
  • Banks, J.; Carson, J.; Nelson B. L.; Nicol, D. “Discrete-Event System Simulation”, Fourth Edition. Prentice Hall – 2004

 

Optimization (45h)
Prof. Anderson Paulo Paiva

Goal: Basic theory and methods for the solution of optimization problems; iterative techniques for unconstrained minimization; linear and nonlinear programming with engineering applications.

Topics:

  • Introduction and review of fundamentals
  • Unconstrained optimization
  • Optimization subject to equality constraints
  • Nonlinear programming
  • Linear programming
  • Selected topics from dynamic programming, large-scale programming, and multicriteria optimization

Reference:

  • Ashok D. Belegundu and Tirupathi R. Chandrupatla, Optimization Concepts and Applications in Engineering