Academic Year 2023-2024



Franca Rinaldi
Unit Credits
Teaching Period
Second Period
Course Type
Prerequisites. Knowledge of the theory and of the main techniques of linear programming and integer linear programming.

Basic definitions and results in graph theory and network flows.

Teaching Methods. Lessons and exercises.
Verification of Learning. The exam consists of an oral test designed to verify the knowledge of the topics covered in the course and the ability to present them adequately, highlighting their salient aspects with a precise and formally correct language.

The exam consists of an oral test aimed at assessing the knowledge of the topics covered in the course and the ability to present them appropriately, highlighting the key aspects and using precise and formally correct language. The test may take into account any additional research conducted by the student on subjects related to the course topics and agreed upon with the teacher.

The grade will be determined according to the criteria approved by the Course Council, which can be consulted at the following link:

More Information. ~
By the end of the course the student should:

Knowledge and understanding:

– know both exact and heuristic techniques for classes of optimization problems which are relevant in the managment of real systems;

– be able to formally express a solution procedure using a programming language or a pseudocode;

Applying knowledge and understanding:

– be able to recognize the main aspects which are relevant in modeling a real optimization problem (variables, constraints, objective function);

– be able to evaluate the complexity of a problem and the effectiveness of an algorithm proposed for its solution;

Autonomy of judgment:

– be able to identify possible types of models and solution procedures for an optimization problem;

– be able to help in analyzing and correctly formulating a model for an optimization problem of the production / industrial world;

Communication skills:

– know the language of graph theory and mathematical optimization;

– be able to present the content of a scientific O.R. article;

Learning skills:

– be able of self-study from the recommanded bibliography;

– know how to formulate appropriate models for the problems discussed in class and for other defined autonomously.

The goal of the course is to describe the main modelling and solving techniques usually adopted for the solution of computationally hard problems, with particular reference to those problems that arise in the management of real systems. Besides classical exact solution techniques, approximation algorithms and local search methauristics will be described and analyzed. The classes of routing problems and machine and personel scheduling problems will be considered in detail.
Lecture notes and other references suggested by the teacher.