First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
COMPUTER SCIENCE
Course unit
OPERATIONS RESEARCH
SCP4065562, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2016/17

Information on the course unit
Degree course First cycle degree in
COMPUTER SCIENCE
SC1167, Degree course structure A.Y. 2011/12, A.Y. 2018/19
N0
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Number of ECTS credits allocated 7.0
Type of assessment Mark
Course unit English denomination OPERATIONS RESEARCH
Website of the academic structure http://informatica.scienze.unipd.it/2018/laurea
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge LUIGI DE GIOVANNI MAT/09

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines MAT/09 Operational Research 7.0

Course unit organization
Period First semester
Year 3rd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Practice 1.5 12 25.5 No turn
Laboratory 1.0 12 13.0 No turn
Lecture 4.5 36 76.5 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019

Examination board
Board From To Members of the board
3 a.a 2018/2019 01/10/2018 28/02/2020 DE GIOVANNI LUIGI (Presidente)
RINALDI FRANCESCO (Membro Effettivo)
CONFORTI MICHELANGELO (Supplente)
DE FRANCESCO CARLA (Supplente)
DI SUMMA MARCO (Supplente)
2 a.a. 2017/2018 01/10/2017 28/02/2019 DE GIOVANNI LUIGI (Presidente)
CONFORTI MICHELANGELO (Membro Effettivo)
DE FRANCESCO CARLA (Membro Effettivo)
DI SUMMA MARCO (Membro Effettivo)
RINALDI FRANCESCO (Membro Effettivo)

Syllabus
Prerequisites: Basic elements of Calculus.
Formal prerequisite courses: "Algebra and Discrete Mathematics".
Target skills and knowledge: Use of mathematical models as decision support tools, and related solution algorithms. Focus on continuous and discrete linear
programming, and on graph optimization. Use of software packages for the solution of optimization problems.
Examination methods: The exam is written and it includes a problem to be formulated with a linear programming model, exercises and theory questions. If needed, an oral discussion can be required. The candidate may also prepare an optional mini-project.
Assessment criteria: The examination evaluates to what extent the student has learned the notions presented (e.g., linear programming modeling, application of the simplex algorithm, application of network optimization algorithms, application of the duality theory, application of the Branch-and-Bound algorithm, tests on all presented notions etc.)
Course unit contents: 1. Optimization problems and models: creating models and using software solvers.

2. Linear programming: simplex theory and method, duality theory and applications.

3. Graph optimization: models and algorithms for optimal spanning trees, shortest paths (Dijkstra and Bellman-Ford algorithms), max flow (Ford-Fulkerson algorithm), min cost flow.

4. Introduction to Integer Linear Programming and Combinatorial Optimization: exact methods (Branch-and-Bound), introduction to heuristics and meta-heuristics (local search based).
Planned learning activities and teaching methods: Classes and labs. During labs, some basic linear programminf models will be implemented, using an algebraic modeling language.
Additional notes about suggested reading: The teacher will provide lecture notes and/or slides containing all the notions requested.
The interested student can find further details on the following references:
- M. Fischetti, Lezioni di Ricerca Operativa, 1999, Libreria Progetto Padova.
- D. Bertsimas, J. Tsitsiklis, Introduction to linear optimization, 1996, Athena Scientific.
- R. K.Ahuja, T. L. Magnanti, J. B. Orlin "Network flows. Theory, algorithms, and applications", 1993, Prentice Hall.
- L. A. Wolsey: "Integer programming", 1998, Wiley.
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Questioning
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)
  • AMPL, IBM Cplex Optimization Suite, Excel Solver

Sustainable Development Goals (SDGs)
Quality Education Decent Work and Economic Growth Industry, Innovation and Infrastructure