Servizi per la didattica
PORTALE DELLA DIDATTICA

Multiobjective Mathematical Programming (didattica di eccellenza vp)

01UFWRP

A.A. 2020/21

Course Language

English

Course degree

Doctorate Research in Gestione, Produzione E Design - Torino

Course structure
Teaching Hours
Lezioni 20
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Della Croce Di Dojola Federico Professore Ordinario MAT/09 1 0 0 0 1
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
*** N/A ***    
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In this course, we will focus on problems relevant to Operations Research where several conflicting objectives have to be minimized to compute a solution. First the basics of multiobjective optimization will be introduced (Pareto optimality, properties on the Pareto front, complexity results) in order for the students to fully capture the important notions. We will notably focus on problems which can be modeled by means of mixed integer linear programming. Second, the different methods for computing Pareto optima will be introduced sketching by the way their main properties, advantages and drawbacks. Third, we will take numerous examples from multicriteria scheduling theory to illustrate how in practice multiobjective optimization problems can be solved. This will be the opportunity to present various multiobjective scheduling models from classic ones to more complex ones. For each of them, heuristic and exact optimization algorithms will be presented. This content will follow my milestone book on multicriteria scheduling • V. T’KINDT, J.-C. BILLAUT. Multicriteria Scheduling: Theory, Models and Algorithms, Springer, 2nd edition, 2006.
In this course, we will focus on problems relevant to Operations Research where several conflicting objectives have to be minimized to compute a solution. First the basics of multiobjective optimization will be introduced (Pareto optimality, properties on the Pareto front, complexity results) in order for the students to fully capture the important notions. We will notably focus on problems which can be modeled by means of mixed integer linear programming. Second, the different methods for computing Pareto optima will be introduced sketching by the way their main properties, advantages and drawbacks. Third, we will take numerous examples from multicriteria scheduling theory to illustrate how in practice multiobjective optimization problems can be solved. This will be the opportunity to present various multiobjective scheduling models from classic ones to more complex ones. For each of them, heuristic and exact optimization algorithms will be presented. This content will follow my milestone book on multicriteria scheduling • V. T’KINDT, J.-C. BILLAUT. Multicriteria Scheduling: Theory, Models and Algorithms, Springer, 2nd edition, 2006.
Modalità mista
Mixed mode
Presentazione report scritto
Written report presentation
P.D.2-2 - Settembre
P.D.2-2 - September
Classroom B (DIGEP) 1. 21/09/2021 : 9h-11h 2. 22/09/2021 : 9h-11h 3. 28/09/2021 : 9h-11h 4. 29/09/2021 : 9h-11h 5. 5/10/2021 : 9h-11h 6. 6/10/2021 : 9h-11h 7. 12/10/2021 : 9h-11h 8. 13/10/2021 : 9h-11h 9. 19/10/2021 : 9h-11h 10. 20/10/2021 : 9h-11h
Classroom B (DIGEP) 1. 21/09/2021 : 9h-11h 2. 22/09/2021 : 9h-11h 3. 28/09/2021 : 9h-11h 4. 29/09/2021 : 9h-11h 5. 5/10/2021 : 9h-11h 6. 6/10/2021 : 9h-11h 7. 12/10/2021 : 9h-11h 8. 13/10/2021 : 9h-11h 9. 19/10/2021 : 9h-11h 10. 20/10/2021 : 9h-11h


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