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Quantitative methods and decision aid

01RLDPH

A.A. 2018/19

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Ingegneria Gestionale (Engineering And Management) - Torino

Course structure
Teaching Hours
Lezioni 60
Esercitazioni in aula 20
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Della Croce Di Dojola Federico Professore Ordinario MAT/09 30 10 0 0 5
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
MAT/09 8 D - A scelta dello studente A scelta dello studente
Valutazione CPD 2018/19
2018/19
Quantitative methods seek in building up rational models for the representation of complex problems and in devising the related solution algorithms. Objective of the course is to deepen the student skills on several major topics of operations research with emphasis on computational complexity, combinatorial optimization and multi-criteria analysis.
Quantitative methods seek in building up rational models for the representation of complex problems and in devising the related solution algorithms. Objective of the course is to deepen the student skills on several major topics of operations research with emphasis on computational complexity, combinatorial optimization and multi-criteria analysis.
At the end of the course the student must be able to determine the computational complexity of given algorithms, to design exact and heuristic algorithms for real world combinatorial optimization problem, to know methods of multi-criteria analysis and use them in relation to specific decision problem situations.
At the end of the course the student must be able to determine the computational complexity of given algorithms, to design exact and heuristic algorithms for real world combinatorial optimization problem, to know methods of multi-criteria analysis and use them in relation to specific decision problem situations.
Bases of operations research with emphasis on linear programming.
Bases of operations research with emphasis on linear programming.
Computational complexity. Elements of graph theory. Combinatorial optimization: exact methods, heuristic approaches, approximation algorithms. Multi-criteria analysis: introduction to multiobjective optimization (pareto-optimality, solution approaches) and to multicriteria outranking methods, ELECTRE methods for sorting decision problems.
Computational complexity. Elements of graph theory. Combinatorial optimization: exact methods, heuristic approaches, approximation algorithms. Multi-criteria analysis: introduction to multiobjective optimization (pareto-optimality, solution approaches) and to multicriteria outranking methods, ELECTRE methods for sorting decision problems.
The exercise classes are related to the topics presented in the course. In the laboratories commercial software tools are used to deal with more complex problems.
The exercise classes are related to the topics presented in the course. In the laboratories commercial software tools are used to deal with more complex problems.
Slides will be provided directly by the teacher. Reference textbooks that contain part of the topics presented in the course are among others • M.R. Garey, D.S. Johnson (1979), Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman and Co. • C.H. Papadimitriou, K. Steiglitz (1982), Combinatorial Optimization. Algorithms and Complexity, Prentice Hall. • V. Vazirani (2001), Approximation Algorithms, Springer. • P. Vincke (1992), Multicriteria decision-Aid, Wiley, Chichester. • L.A. Wolsey (1999), Integer Programming and Combinatorial Optimization, Wiley.
Slides will be provided directly by the teacher. Reference textbooks that contain part of the topics presented in the course are among others • M.R. Garey, D.S. Johnson (1979), Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman and Co. • C.H. Papadimitriou, K. Steiglitz (1982), Combinatorial Optimization. Algorithms and Complexity, Prentice Hall. • V. Vazirani (2001), Approximation Algorithms, Springer. • P. Vincke (1992), Multicriteria decision-Aid, Wiley, Chichester. • L.A. Wolsey (1999), Integer Programming and Combinatorial Optimization, Wiley.
Modalità di esame: Prova scritta (in aula);
Exam: Written test;
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Written test;
Written exam.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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