OPERATIONS RESEARCH

OPERATIONS RESEARCH

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iten
Code
80155
ACADEMIC YEAR
2017/2018
CREDITS
9 credits during the 1st year of 8733 Computer Engineering (LM-32) GENOVA

7 credits during the 3nd year of 8760 Mathematics (L-35) GENOVA

7 credits during the 1st year of 9011 Mathematics (LM-40) GENOVA

SCIENTIFIC DISCIPLINARY SECTOR
MAT/09
LANGUAGE
Italian
TEACHING LOCATION
GENOVA (Computer Engineering)
semester
1° Semester

OVERVIEW

The Course introduces to optimization models and methods for the solution of decision problems. It is structured according to the basic topics of problem modelling, its tractability, and its solution by means of algorithms that can be implemented on computers.  Case studies from IT are presented and investigated.

AIMS AND CONTENT

LEARNING OUTCOMES

The Course introduces optimization models and methods that can be used to solve decision-making problems. It is part of the fundamental themes of problem modeling, study of computational handling, and resolution through algorithms that can be implemented on a computer. Various application contexts are considered, and some "case studies" in the IT field are discussed in detail. The aim of the course is to acquire the skills to deal with application problems by developing models and methods that work efficiently in the presence of limited resources. Students will be taught to: interpret and shape a decision-making process in terms of an optimization problem, identifying decision-making variables, the cost function to minimize (or the merit digit to maximize) and constraints; Framing the problem in the range of problems considered "canonical" (linear / nonlinear, discrete / continuous, deterministic / stochastic, static / dynamic, etc.); Realizing the "matching" between the solving algorithm (to choose from existing or designing) and an appropriate processing software support.

AIMS AND LEARNING OUTCOMES

The students will be taught to:

- interpret and shape a decision-making process in terms of an optimization problem, identifying the decision-making variables, the cost function to minimize (or the figure of merit to maximize), and the constraints;

- framing the problem in the range of problems considered "canonical" (linear / nonlinear, discrete / continuous, deterministic / stochastic, static / dynamic, etc.);

- realizing the "matching" between the solving algorithm (to choose from existing or to be designed) and an appropriate processing software support.

 

The students will be taught to:

- interpret and shape a decision-making process in terms of an optimization problem, identifying decision-making variables, the cost function to minimize (or the merit digit to maximize) and constraints;

- framing the problem in the range of problems considered "canonical" (linear / nonlinear, discrete / continuous, deterministic / stochastic, static / dynamic, etc.);

- realizing the "matching" between the solving algorithm (to choose from existing or designing) and an appropriate processing software support.

Teaching methods

Lectures and exercises

SYLLABUS/CONTENT

INTRODUCTION TO OPERATIONS RESEARCH AND MANAGEMENT SCIENCE

LINEAR PROGRAMMING

DUALITY

INTEGER PROGRAMMING

GRAPH AND NETWORK OPTIMIZATION

CASE STUDIES FROM ICT

COMPLEXITY THEORY

DYNAMIC PROGRAMMING

NONLINEAR PROGRAMMING

RECOMMENDED READING/BIBLIOGRAPHY

Lecture notes provided by the teacher

TEACHERS AND EXAM BOARD

Ricevimento: By appointment

Exam Board

MARCELLO SANGUINETI (President)

DANILO MACCIO'

MAURO GAGGERO

FEDERICA BRIATA

LESSONS

Teaching methods

Lectures and exercises

LESSONS START

September 18, 2017

EXAMS

Exam description

Written