Optimization

Code:

M470

Acronym:

M470

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2012/2013 - 2S

Active?	Yes
Web Page:	http://www.fc.up.pt/dmat/engmat/otimizacao
Responsible unit:	Department of Mathematics
Course/CS Responsible:	Master in Mathematical Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:ENM	20	PE do Mestrado em Engenharia Matemática	1	-	7,5	-

Teaching language

Portuguese

Objectives

It is intended that students 1. Become familiar with various problems that can be modeled by linear programming (LP), integer programming (IP), binary integer programming (GDP) or mixed (PIM) and nonlinear programming. 2nd. Acquire skills in modeling and solving algorithmic real situations common in many scientific and economic activities. 3rd. Become familiar with key theoretical concepts, methods and algorithms of linear programming (LP), integer programming (IP), binary integer programming (GDP) or mixed (PIM) and dynamic programming in particular duality, complementarity, and modeling using Lagrangean flows in Networks and others.

Learning outcomes and competences

The aim is for students to acquire skills in algorithmic modeling and solving real situations common in many scientific and economic activities.

Working method

Presencial

Program

In the academic year 2012-2013, the course guide will be at the following book:

Modeling Management Science,
S. Christian Albright, Indiana University, School of Business
Wayne L. Winston, Indiana University
936pp
Published by Cengage Learning, © 2012

One. Introduction to Modeling.
2nd. Introduction to Spreadsheet Modeling.
3rd. Introduction to Optimization Modeling.
4th. Linear Programming Models.
5th. Network Models.
6th. Optimization Models with Integer Variables.
7th. Nonlinear Optimization Models.
Eight. Evolutionary Solver: An Alternative Optimization Procedure.
9th. Decision Making Under Uncertainty.
10. Introduction to Simulation Modeling.
11. Simulation Models.
12. Inventory Models.
13. Queueing Models.
14. Regression and Forecasting Models

  Appointments available at http://www.fc.up.pt/dmat/engmat/otimizacao

Mandatory literature

S. Christian Albright, Indiana University, School of Business Wayne L. Winston, Indiana University; Management Science Modeling, Cengage Learning, 2012

Teaching methods and learning activities

Teaching using various models in spreadsheets (Excel). Analysis of case studies displayed by students in class.

Software

Excel 2010 com add-ins fornecidos pelo docente

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Attendance (estimated)	Participação presencial	85,00
Tests during the semester	Teste	5,00	100,00	2013-06-06
	Total:	-	100,00

Eligibility for exams

The distributed evaluation consist of (i). Two tests, each totaling 10 (ten), to be held throughout the semester, the following dates: 1st test: April 11 (Thursday), 2013. 2nd Test: June 3 (Monday), 2013. Students will be approved provided they obtain a grade equal or higher than 10 = sum of the total score of the two tests to be held throughout the semester, with a minimum of four (4) values for each test.

Students not approved by distributed evaluation must pass an examination at the time of appeal.

Calculation formula of final grade

Special assessment (TE, DA, ...)

The tests required under special statutes contain a written test which may be preceded by an oral round, to assess whether the student is in a position of trying to get approved minimum discipline in the written test.

Classification improvement

Grade improvement will be made in consideration of the time for appeal.