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Second Semester 
Teaching style
Lingua Insegnamento

Informazioni aggiuntive

Course Curriculum CFU Length(h)
[60/73]  INFORMATICS [73/00 - Ord. 2017]  PERCORSO COMUNE 6 48


1. Knowledge and understanding skills.
The course is designed for the students of the 1st year of the Master Degree in Computer Science.
This course aims to provide students with a deep knowledge in the theory and practice of (Integer) Linear Programming, which has relevant applications in computer science, economics, engineering, as well as a number of other domains. The goals of the course are the following:
• To teach students how to model several problems by (Integer) Linear Programming
• To present the state-of-the-art in the theory and practice for solving (Integer) Linear Programming problems.
• To provide students with a rigorous analysis of algorithms for (Integer) Linear Programming.
2. Ability to apply knowledge and understanding.
Students must apply the methods presented in the course to solve realistic problems, which are similar to those faced in the lectures. In the oral exam student must explain how some algorithms work.
3. Autonomy of judgment.
The modelling stage will be put in the position of critically thinking at the problem setting, evaluating which data are requested in their formulation. Students must also evaluate the most suitable algorithms to solve specific models.
4. Communicative Skills.
Communicative skills will be further evaluated in the oral exam.
5. Learning Skills.
The course provides students with sufficient preparation to understand more advanced mathematical texts and makes them able to expand their knowledge autonomously in the future.


1. Knowledge. The course would benefit from a good understanding of the basic concepts of Discrete Mathematics and Numerical Analysis, which can be learned both in the Bachelor Degree Program and in the Master Degree Program.
2. Skill. Students must be able to read and formalize algorithms’ pseudocodes.
3. No a-priori competences is requested.
No exam has to be passed before the exam of Decision Science.


• Mathematical Programming
• Linear Programming Models
• The simplex Method
• Duality theory
• Integer Programming
• Computational Complexity Theory

Teaching Methods

The course consists of 48 hours of lectures. They cover theoretical concepts, as well as several exercises to review and reinforce the theoretical concepts. Finally, the professor provides regular support to students throughout the course by ad-hoc meetings and e-mails.

Verification of learning

Students must demonstrate their knowledge of the specific terminology, the ability to solve a realistic problem and the theoretical concepts presented in the lectures. Students are evaluated in two stages: a written exam and an oral exam. The written exam has three exercises and the maximum mark is 21. Two questions are typically made in the oral exam, whose maximum mark is 10. The final mark is the sum of the former ones:
• The final mark ranges between 18/30 and 22/30 in the case of sufficient knowledge of the specific terminology, correct application of the methodological concepts and sufficient presentation of the concepts and results.
• The final mark ranges between 22/30 and 26/30 in the case of good knowledge of the terminology, good application of the methodological concepts and a good presentation of concepts and results.
• The final mark ranges between 27/30 and 30 cum laude in the case of an excellent mastery of specific terminology, a critical application of the methodological concepts and a clear display of concepts and results.
Students are advised to check their preparation during the lectures. They will test their skills by practicing with exercises and comparing their results to those presented by the Professor.


Matteo Fischetti. Lezioni di Ricerca Operativa. Kindle Publishing. 2018

Mauro Dell’Amico. 120 esercizi di Ricerca Operativa (seconda edizione). Pitagora Editrice, Bologna.

More Information

The main teaching-supporting tool is the platform elearning platform (https://elearning.unica.it/), where additional information is available (e.g. a course diary reporting the topics of each lesson and further teaching files).

Questionnaire and social

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