OVERVIEW

The course of Operations Research provides skills related to model building and solving decision-making problems formulated in terms of optimization problems.

AIMS AND CONTENT

LEARNING OUTCOMES

The course provides the basic knowldge of optimization methods to solve decision-making problems. In particular, the course will provide knowledge to model from the mathematical viewpoint a decision problem, and solve it through linear programming, linear integer programming, nonlinear programming, and optimization over graphs.

AIMS AND LEARNING OUTCOMES

The course aims to study the main optimization methods for solving decision problems. In more detail, the course aims to provide students with the basic skills for formalizing in mathematical terms and then solving decision problems, in which the optimal decision must be made in the context of several possible decisions, based on suitable criteria. In particular, the course presents the concepts of decision variables, objective function, and constraints of an optimization problem, as well as the basics of real linear programming, integer linear programming, nonlinear programming, and optimization over graphs.

For all topics, both methodological and applied aspects are presented. The various concepts are exposed through theoretical lectures and by solving exercises, as well as through the software implementation of some example problems.

At the end of the course, the student will be able to construct a mathematical model of a decision-making process and to choose and apply the most appropriate algorithm for its solution. 

PREREQUISITES

Basic knowledge of Calculus.

TEACHING METHODS

Traditional lessons.

SYLLABUS/CONTENT

- Introduction to mathematical programming and decision problems

- Linear programming

- Integer inear programming

- Nonlinear programming

- Optimization over graphs

- Software applications for mathematical programming

RECOMMENDED READING/BIBLIOGRAPHY

Handouts provided by the lecturer.

Books for possible further study:

[1] Hillier, Lieberman – Introduction to operations research. McGraw-Hill, 2004.

[2] D. Bertsimas, J.N. Tsitsiklis – Introduction to linear optimization. Athena Scientific, 1999.

[3] D. Luenberger, Y. Ye – Linear and nonlinear programming. Springer, 2008.

[4] D. Bertsekas – Nonlinear Programming. Athena Scientific, 1999.

TEACHERS AND EXAM BOARD

Exam Board

MASSIMO PAOLUCCI (President)

SILVIA SIRI

LESSONS

LESSONS START

https://corsi.unige.it/en/corsi/10716/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Written examination possibly supplemented by oral examination.

ASSESSMENT METHODS

Upon completion of the course, students should demonstrate understanding of the concepts seen in lectures and be able to discuss them in appropriate language. In addition, students should demonstrate ability to construct a mathematical model of a decision-making process and to choose and apply the best algorithm for its solution.

Exam schedule

FURTHER INFORMATION

None.