Operational Research

Code:

EM0039

Acronym:

IO

Keywords
Classification	Keyword
OFICIAL	Management

Instance: 2014/2015 - 1S

Active?	Yes
Web Page:	https://sites.google.com/a/gcloud.fe.up.pt/io-2014-2015-miem/
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Master in Mechanical Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIEM	132	Syllabus since 2006/2007	4	-	6	52	162

Teaching language

Portuguese

Objectives

BACKGROUND

This course focuses on the application of analytical methods to take better decisions and provides students with tools for modelling and optimization that will be very useful in various roles in several types of organizations (industry and services).

SPECIFIC OBJECTIVES

The main objective of this course is, through the creation of models, develop skills for analysing a wide range of real situations. These competencies are based on the ability to recognize the key problem in a non-structured situation, on the ability to develop a framework for analysing and treating the problem and on the application of analytical methods for its resolution.

LEARNING OBJECTIVES

Endow the students with the skills to:
• identify and address decision problems in a structured way;
• build models of decision problems;
• identify and use analytical methods to obtain solutions for the models, that should act as a support for informed decisions;
• use spreadsheets to analyse and obtain solutions for the models;
• extract information from the models to communicate and motivate organizational changes.

Learning outcomes and competences

 This course will contribute to the acquisition, by the students, of the following CDIO competencies:

1. Technical Knowledge and Reasoning

1.1. Acquire proficiency with the necessary knowledge of basic sciences and be able to use them in the formulation, discussion and resolution of problems of their area;

The contents of this course builds directly on knowledge of Algebra, Mathematical Analysis, Probability Theory and Statistics, organizing them, developing them and applying them in the context of supporting the resolution of decision problems. By nature these skills cut across all areas of Engineering and belong to their basic sciences. On the other hand, during class there is a concern to use examples and cases from the scientific area of each course, bridging the gap between the basic sciences and area of the degree.

The assessment in the course addresses these skills.

2. Personal and Professional Skills and Attitudes

2.1. Engineering reasoning and problem solving
2.1.1 problem identification and formulation
2.1.2 modelling
2.1.3 estimation and qualitative analysis
2.1.4 analysis with uncertainty
2.1.5 solution and recommendation

Exactly these points can describe the teaching methodology and the contents of this course. Thus, the formulation of problems according to each of the models studied throughout the course, their modelling and resolution are an on-going activity. The qualitative analysis is addressed in the sensitivity analysis, while uncertainty is addressed in Decision Theory and Project Planning and Control.

The assessment in the course addresses these skills.

2.2 Experimentation and knowledge discovery
2.2.1 hypothesizing
2.2.2 literature search
2.2.3 Experimental investigation
2.2.4 test and defence of hypotheses

2.3 systemic thinking
2.3.1 holistic thinking
2.3.2 emergency and interaction between systems
2.3.3 prioritization and focus
2.3.4 trade-offs, judgment and balancing the resolution

These skills are addressed indirectly, and may be more directly described as:

(1) analysis competencies - ability to conceptualize, formulate and solve unfamiliar problems;
(2) project competencies - ability to address with creativity, unfamiliar, complex and technically uncertain problems;
(3) research competencies - ability to model and evaluate the applicability of emerging techniques (description of the criteria according to Euro-ACE).

Problem design and modelling, involving the identification of the applicable family of models and the development and evaluation of original models, clearly contributes to these skills. The topics related with sensitivity analysis contribute to the understanding of concepts related to trade-offs and balancing.

The assessment in the course addresses these skills.

The learning objectives are related to the competencies (knowledge, skills and attitudes that students should have) and are expressed as statements about things that students should be able to explain, calculate, deduct, design, etc ...

For all program topics, detailed learning objectives are available from the web site, along with educational materials to support learning.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Basic courses in algebra, statistics and probability theory.

Program

• What is Operations Research?
• History and methodology of Operations Research.
• Decision Theory.
• Modeling.
• Linear Programming.
• Simplex Method.
• Integer Programming.
• Transportation problems and Flows in generic networks.
• Assignment Problems.
• Maximum Flow Problems
• Minimum Path Problems.
• Other network problems.
• Project planning and control CPM, PERT.
• Queueing Theory.

Mandatory literature

Docentes de IO|DEGI|FEUP; Documentação de apoio a Investigação Operacional (Available at the course site)

Complementary Bibliography

Hillier, Frederick S.; Introduction to operations research. ISBN: 0-07-118163-6
Hamdy A. Taha; Operations research. ISBN: 0-13-281172-3 (brochada)

Teaching methods and learning activities

Lectures: The themes are presented using active learning methods. The problems are illustrated with examples.

Recitations: The problems from a list of proposed problems are discussed and solved by the students.

Software

Excel Solver

keywords

Physical sciences > Mathematics > Applied mathematics > Operations research
Social sciences > Economics > Management studies > Industrial management

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	50,00
Teste	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	112,00
Frequência das aulas	50,00
Total:	162,00

Calculation formula of final grade

DISTRIBUTED EVALUATION (closed book)
Sum of the top 10 exercises of each student out of the 12 exercises proposed in the practical classes. Each exercise will be rated on a scale from 0 to 1. Final rate 0-10.

EXAM (open book)
Final Exam, 0-10.