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Course unit
METHODS AND MODELS FOR COMBINATORIAL OPTIMIZATION
SC01122975, A.A. 2016/17
Information concerning the students who enrolled in A.Y. 2015/16
ECTS: details
Type |
Scientific-Disciplinary Sector |
Credits allocated |
Educational activities in elective or integrative disciplines |
MAT/09 |
Operational Research |
6.0 |
Course unit organization
Period |
First semester |
Year |
2nd Year |
Teaching method |
frontal |
Type of hours |
Credits |
Teaching hours |
Hours of Individual study |
Shifts |
Practice |
0.5 |
4 |
8.5 |
No turn |
Laboratory |
1.5 |
12 |
25.5 |
No turn |
Lecture |
4.0 |
32 |
68.0 |
No turn |
Examination board
Board |
From |
To |
Members of the board |
6 a.a. 2018/2019 |
01/10/2018 |
28/02/2020 |
DE GIOVANNI
LUIGI
(Presidente)
DI SUMMA
MARCO
(Membro Effettivo)
CONFORTI
MICHELANGELO
(Supplente)
DE FRANCESCO
CARLA
(Supplente)
RINALDI
FRANCESCO
(Supplente)
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5 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)
|
Prerequisites:
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Basic notions of Operations Research, Linear Programming, and computer programming. |
Target skills and knowledge:
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Basic use of advanced decision support methods for modelling and solving combinatorial optimization problems. Use of mathematical and algorithmic tools for solving optimization problems of practical relevance by software packages and optimization libraries. |
Examination methods:
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Oral examination about course contents. Each student may chose to present a short project concerning models and exact/heuristic solution methods for a realistic application of combinatorial optimization. |
Assessment criteria:
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The examination evaluates to what extent the student has learned the notions presented and her/his ability to apply them to the solution of practical combinatorial optimization problems. |
Course unit contents:
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1. Advanced linear programming and duality with applications: primal-dual simplex, column generation, applications to network
optimization.
2. Advanced methods for Integer Linear Programming (ILP): Branch & Bound and relaxation techniques, alternative ILP formulations,
cutting planes method and Branch & Cut, application to relevant examples (Traveling Salesman Problem, location, network design etc.).
3. Meta-heuristics for combinatorial optimization: local search based, evolutionary algorithms.
4. Application of graph modeling and optimization.
5. Labs: optimization software packages and libraries. |
Planned learning activities and teaching methods:
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Classes. Labs. Discussion about case studies. During labs, some basic optimization algorithms will be implemented, both exact (using integer programming libraries) and heuristic. |
Additional notes about suggested reading:
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Lecture notes provided by the teacher. Articles from scientific journals. |
Textbooks (and optional supplementary readings) |
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