Management for Engineering

Code:

DLIT0029

Acronym:

GE

Keywords
Classification	Keyword
OFICIAL	Engineering Systems

Instance: 2022/2023 - 2S

Active?	Yes
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Doctoral Program in Leaders for Technological Industries

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
DLIT	0	Syllabus since 2009/10 - LTI	1	-	6	30	162
EAIEP	0	Syllabus since 2009/10	1	-	6	30	162

Teaching language

Portuguese and english

Objectives

Understand the complexity and the quantitative and qualitative aspects of decision-making processes.

Study and understand the general concept, structure and components of a Decision Support System (DSS), as well as approaches to design and implement DSS. Present and discuss examples of DSS, with special emphasis on aspects related to the user interfaces.

Discuss several Decision Theory topics in practical applications, and make an introduction to Multi-criteria approaches.

Introduce the fundamental concepts and principles of Optimization, through models and applications of Linear Programming and Combinatorial Optimization, and generic heuristic techniques (meta-heuristics) to solve problems of interest in a wide range of applications.

Learn the fundamentals of discrete-event Simulation, and develop skills to apply simulation models to address problems of practical interest in Operations Management.

Understand and apply the fundamental concepts of “investment projects analysis”, in a decision-making perspective.

 

 

Learning outcomes and competences

The competences to be acquired by the students, as well as the results of the learning process, derive directly from the satisfaction of the indicated objectives.

Working method

Presencial

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

Basic knowledge of Operations Research.

Program

General structure and components of a DSS. Quantitative methods for decision making. Models. Qualitative aspects in decision making. Structuring decision problems
 

2. Decision Theory and Multicriteria Analysis.

Uncertainty and risk situations. Structuring alternatives and decision criteria. Decision trees. Decision problems with multiple criteria. Analytic Hierarchy Process (AHP). Sensitivity analysis and 'what-if' analysis.

3. Formulation of problems and optimization models.

Linear programming and extensions. Optimization methods and computational tools for optimization. Transportation and assignment problems. Network problems: flows, paths and trees - models and algorithms. Combinatorial optimization problems: heuristic methods for practical problem solving - design and implementation. Integer Programming.

 
4. Combinatorial Optimization and Heuristic Techniques.

Combinatorial Optimization: models and applications. Knapsack problems; set covering and location problems; distribution and collection problems. Heuristics and meta-heuristics: local search, "Simulated Annealing", Taboo search; Genetic Algorithms.

5. Simulation.

Simulation models: general structure and scope. Modelling methods: main characteristics and scope of application. Interactive visual simulation. Construction and validation of simulation models. Applications to practical Operations Management problems.


6. Analysis of investment projects.

Brief introduction and fundamental concepts. Analysis dimensions and stakeholder involvement. Multi-criteria perspective in the analysis of investment projects.

7. Methodologies for the design of DSS and tools for their implementation.

Modularity and prototyping. Organizational aspects in the design of a DSS. Specification and development of a DSS: examples. Discussion of case studies.

Mandatory literature

Stephen G. Powell; Management science. ISBN: 978-0-470-03840-6

Complementary Bibliography

Robert T. Clemen; Making hard decisions. ISBN: 0-534-92336-4

Comments from the literature

Lecture slides are made available, as well as some additional articles and small exercise collections.

Teaching methods and learning activities

The course is organized in sessions with the presentation of the programmatic themes, the discussion of cases, and the resolution of small illustrative problems.

The reports to be presented (as part of the assessment) will essentially be done outside of classes.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	50,00
Apresentação/discussão de um trabalho científico	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	120,00
Frequência das aulas	42,00
Total:	162,00

Eligibility for exams

Students have to attend classes (according to the "General Evaluation Rules" of FEUP Pedagogical Council).

Calculation formula of final grade

FE (final exam) - 0 to 20 points (minimum 10)

A (assignment) - 0 to 20 points (minimum 10)

Final grade: 0.50 FE + 0.50 A

Observations

The improvement of the final grade can only be done on the exam component.

Students cannot repeat the assignment.

The formula for the final grade is the same.