Quantitative Methods for Management

Code:

EIG0038

Acronym:

MQAD

Keywords
Classification	Keyword
OFICIAL	Quantitative Methods

Instance: 2015/2016 - 1S

Active?	Yes
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Master in Engineering and Industrial Management

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIEIG	53	Syllabus since 2006/2007	5	-	7	56	189

Teaching language

English

Objectives

The managers of every company – from the private or public sectors – have to make decisions on how to allocate the organization resources. Being part of the necessary information to take decisions quantitative, the managers of today’s world must be able to assess, analyse and use it. The aim of this course is to provide the students the suitable analytical skills and data treatment tools and quantitative models to support decision making procedures.

Learning outcomes and competences

To allow the students to create, update and develop broad but simultaneously in-depth competences in quantitative methods for management, which may allow them to: 

 

- understand the role of demand planning in organizations, and master the way a forecasting system must be implemented:

 

- implement and use direct extrapolation statistical methods, to forecast the behavior of non-controllable variables;

 

-identify optimization problems, particulary in the context of operations management and combinatorial optimization,  and approach them in a structured way;

- define the most adequate abstraction level to model optimization problems for an algorithmic approach to their resolution.

- identify the algorithmic techniques to solve a particular optimization problem;

- use heuristics and metaheuristics methods to obtain solutions for the problems;

 

- implement, test and validate, search methodologies to solve different classes of optimization problems.

Working method

Presencial

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

Operations Research

Statistics

Program

1st part: Forecasting Methods FORECASTING and DECISIONS MAKING : Role of the FM in decision processes. Classification of the FM. Quantitative Methods: methods based on time series and causal methods. Underlying hypotheses and conditions of applicability. FM Selection. ANALYSIS OF DATA: How to present data. How to detect and handle exceptional data points. Advantages and risks of the aggregation of data. ANALYSIS OF TIME SERIES: Introduction. Regression (revision of concepts studied in Statistics). Classical decomposition. Exponential Smoothing Methods

2nd part: Overview of models, applications and solution techniques of Combinatorial Optimization; Comparison between exact and approximate methods; algorithms performance; constructive and improvement heuristics. Metaheuristics: introduction; examples of population-based metaheuristics and neighborhood based.

 

3rd part: Management Science: analysis of quantitative models and tools that support the best management practice of operations in state-of-the art companies; Solving a real-world problem.

Mandatory literature

Joseph F. Hair, Bill Black, Barry Babin, Rolph E. Anderson, Ronald L. Tatham; Multivariate Data Analysis (6th Edition), Prentice Hall; 6 edition (October 28, 2005), 2005. ISBN: 0130329290
Burke, Edmund K. 340; Search Methodologies. ISBN: 978-0387-23460-1
Reeves, Colin R. 340; Modern heuristic techniques for combinatorial problems. ISBN: 0-07-709239-2
Makridakis, Spyros; Forecasting methods for management. ISBN: 0-471-60063-6

Teaching methods and learning activities

The course relies on a combination of case discussions, lectures, readings, and assignments.

Theoretical classes: Presentation sessions and case studies discussion.

Practical classes: problem solving (based on worksheets)..

1st part: groups of students have to analyse a case study on Forecasting Methods and produce and present a report.

2nd part. Analysis of scientific papers on Operations Management and Combinatorial Optimization. Each group must analyse and present a scientific paper, which will then be discussed by a second group of students. Active learning is motivated throughout the course.

 

3rd part: students must develop and implement a heuristic procedure to solve real-world applications and write a small report, with a scientific style.

keywords

Technological sciences

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Defesa pública de dissertação, de relatório de projeto ou estágio, ou de tese	5,00
Participação presencial	28,00
Teste	27,00
Trabalho escrito	40,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	12,00
Estudo autónomo	50,00
Frequência das aulas	52,00
Trabalho de campo	75,00
Total:	189,00

Eligibility for exams

Presence at the practical classes for the discussion of the forecasting methods case study, scientific papers and intermediate results of the combinatorial optimization assignment.

Calculation formula of final grade

Final grade is a weighted average of the individual marks obtained in the Forecasting Methods (FM) case study (0.20) –P1.1, individual test on FM (0.30)-P1.2, analysis of a paper and development of the algorithm and respective report (0.30)-P2.1 and an individual test on optimization (0.20)-P2.2.

 

P = 0.20*P1.1 + 0.30*P1.2 + 0.30*P2.1 + 0.20*P2.2

Special assessment (TE, DA, ...)

Students with a special status (working students, military personnel, and high-level competition athletes) can opt to be assessed as normal students.

Alternatively:

Instead of separate components P1.1, P1.2, P2.1 and P2.2 they should answer a written final examination (date to be defined) with those 4 parts.

 

It is estimated that this exam will last for about 3 hours.

Classification improvement

Students may repeat in the period of exams at the end of the semester the P1.2 and P2.2 short exams with the lowest mark (just one).

 

Students may also improve their marks in the following academic year, by improving the components they wish to.