OVERVIEW

Decision making problems arising in a large number of application contexts can be modeled and solved by means of optimization methods and algorithm of Operations Research. This course aims at presents a set of mathematical models and methods from Operations Research  for solving decision problems with reference to natural risk and emergency management. Basic notion of optimization and mathematical modeling are provided. Specific attention is devoted to linear mathematical programming techniques and graph and networks.

AIMS AND CONTENT

LEARNING OUTCOMES

The course presents a set of mathematical models and methods for solving decision problems with a particular reference to natural risk and emergency management. The purpose of this course is to provide the students with competences in using a set of models for problem solving. In particular, the course mainly considers optimization problems faced by mathematical programming techniques and problems on graph and networks. 

AIMS AND LEARNING OUTCOMES

The main objective is to provide students with the skills to define mathematical programming models to solve a series of decision problems by formulating them as optimization problems. Students will be able to solve continuous and mixed integer programming problems using appropriate methods and algorithms. Students will be able to solve problems using networks flow models and graphs. These models represent fundamental optimization tools for their possible applications in the management of natural risk and emergencies.

TEACHING METHODS

The course consists of classroom lectures.

SYLLABUS/CONTENT

Introduction to decisional problems and models.

Optimization problems and optimality conditions.

Basic concepts of non-linear mathematical programming.

The process of problem formulation by means of quantitative models.

Linear programming; graphic formulation and solution of linear programs; the simplex algorithm; duality theory; sensitivity analysis.

Integer programming and combinatorial optimization; the methods of cutting-planes and branch-and-bound.

Graph theory; the shortest paths problem; the minimum spanning tree problem. Network problems; min cost flow and max flow problems.

Some concepts of multi-objective optimization

Basic concepts of the theory of complexity.

RECOMMENDED READING/BIBLIOGRAPHY

Introduction to Operations Research, 9/e

Frederick S Hillier, Stanford University

Gerald J Lieberman, Late of Stanford University

ISBN: 0073376299

McGraw-Hill Higher Education, 2010

TEACHERS AND EXAM BOARD

Exam Board

ROBERTO SACILE (President)

CHIARA BERSANI

RICCARDO MINCIARDI

MICHELA ROBBA

ADRIANA SACCONE

MAURO GAGGERO (President Substitute)

MASSIMO PAOLUCCI (President Substitute)

MARCELLO SANGUINETI (President Substitute)

LESSONS

LESSONS START

https://courses.unige.it/10553/p/students-timetable

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Written exam text and oral exam (optional after passing the written text). The students who want to take the exam must register online and send an email to the professor.

Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

ASSESSMENT METHODS

The students will be asked to solve linear and integer programming problems using the learnt algorithms and applying concepts from theory. They have to be able to sove problems on graphs and networks. They have to demostrate to know the basic concepts of multi-criteria decision making.

Exam schedule